CN105612546B

CN105612546B - Apparatus, server, system and method for energy measurement

Info

Publication number: CN105612546B
Application number: CN201580000344.5A
Authority: CN
Inventors: 催鍾雄; 裵鉉修; 李曉爕; 李善正; 咸日漢; 李惠
Original assignee: ENCORED TECHNOLOGIES Inc
Current assignee: ENCORED TECHNOLOGIES Inc
Priority date: 2014-07-11
Filing date: 2015-07-13
Publication date: 2020-05-22
Anticipated expiration: 2035-07-13
Also published as: JP2016165209A; CN105717355A; JP2017153359A; WO2016006977A1; JP6309100B2; JP2016185059A; CN105703358B; JP6232021B2; CN105612546A; JP6232020B2; JP2016527869A; CN105703358A

Abstract

Accordingly, embodiments herein provide a method of load balancing in an energy measurement information system. The method includes collecting power information at a snapshot extraction frequency by a power information collection unit. The snapshot extraction frequency is within a range. Also, the method includes detecting an operation state of at least one load device by an operation state extraction unit at a snapshot extraction frequency. The operating condition is one of a steady state and a transient state. In addition, the method includes generating a data set by a data set generating unit, the data set including a representative snapshot of the power information being generated when the normal state is detected; a data set including a plurality of snapshots of power information is generated upon detection of a transient.

Description

Apparatus, server, system and method for energy measurement

Cross reference to related patent applications

The present patent application claims priority from korean patent application No. 10-2014-0087330 filed on 11/7/2014 and korean patent application No. 10-2015-0080222 filed on 5/6/2015, the disclosures of which are incorporated herein by reference.

Technical Field

The present invention relates to an energy measurement information system, and more particularly, to a system and method for load balancing between devices and servers.

Background

Conventional energy measuring devices can only measure total power usage information, which is generated by all individual load devices associated with the energy measuring device, through Advanced Metering Infrastructure (AMI), automatic meter reading system (AMR), digital power instrumentation, or the like in the prior art. In order to extract energy usage information for each load device, either multiple energy measurement devices are installed, or a single energy measurement device with multiple sensors needs to be installed in the distribution board. When one energy measuring device is installed for each load device, more installation space is required, thereby increasing the overall cost of the system. When multiple sensors are used in a power distribution panel, the overall cost of the system increases depending on the multiple sensors employed. Furthermore, there is a limit to capturing energy usage information for each load device.

In order to solve the above problems, various mechanisms for efficiently extracting energy usage information of each load device at a power input point have been proposed. In one mechanism, a scheme is indicated to extract energy usage information for each load device through a series of computer operations performed to measure signal information such as current, voltage, power, etc. The measured signal information of each load device is then directly transmitted to the designated server. However, it is important to develop an energy measuring device capable of performing a priori signal information processing in order to flexibly process, store and manage mass data of a server. A priori signal information processing is associated with signal information samples and aggregations of particular data sets (e.g., data corresponding to the same load device). In this case, the information processed needs to maintain the resolution at a level where each individual load device can be distinguished while the server computer is running.

Accordingly, there remains a need for robust systems and methods for measuring and labeling, respectively, a plurality of energy usage information of load devices connected to a power input point.

Disclosure of Invention

Accordingly, embodiments herein provide an energy measurement device at a power input point for load balancing of an energy measurement information system. The energy measuring device comprises a power information acquisition unit arranged to acquire power information at a snapshot extraction frequency. The snapshot extraction frequency is within a range. The energy measuring device comprises an operating state extraction unit which is arranged to detect an operating state of the at least one load device with a snapshot extraction frequency. The operating condition is one of a steady state and a transient state. Furthermore, the energy measuring device comprises a data set generating unit arranged to generate a data set comprising one representative snapshot of the power information upon detection of the normal state; and generating a data set including a plurality of snapshots of the power information when the transient is detected.

In one embodiment, the range is 10 to 900 times per second.

In one embodiment, the representative snapshot is selected based on the measurement method.

In one embodiment, the energy measurement apparatus comprises a transmission unit arranged to transmit a representative snapshot of power information upon detection of a normal state; and transmitting multiple snapshots of the power information when the transient is detected.

In an embodiment, the power information acquisition unit is arranged to acquire power information. The power information includes power signals of a plurality of load devices at a power input point.

In one embodiment, the snapshot of the power information is one snapshot of a voltage snapshot and a current snapshot of a waveform having a predetermined period as the power information.

Accordingly, embodiments herein provide a server for load management in an energy measurement information system. The server comprises a controller unit arranged to calculate a correlation of the signals so as to reflect power information of the at least one load device from a snapshot of the power signals. The snapshot of the power signal is associated with one of a voltage snapshot and a current snapshot of the waveform, and the waveform having a predetermined period is measured by the remote energy measurement device. Furthermore, the controller unit is configured to classify the power information into one of an on-stream and an off-stream according to a composition unit that composes the at least one load device according to the signal correlation. Furthermore, the controller unit is configured to generate a data set of at least one load device based on the classified power information.

In one embodiment, one of a multi-step operation and a continuous shift operation is classified into an association group by one of the on-stream and off-stream operations with respect to the same load device based on the signal correlation.

In one embodiment, the signal correlation includes at least one of a voltage correlation, a current correlation, a high frequency distortion, a power signal distortion, a real power correlation, and a reactive power correlation.

In one embodiment, the controller unit is arranged to map and rebin the sorted data sets according to the time domain; and labeling the recombined data set.

In one embodiment, the operating state is used to differentiate the distribution plane of each load device.

Accordingly, embodiments herein provide an energy measurement information system. The energy measurement information system includes an energy measurement device configured to collect power information at a snapshot extraction frequency. The snapshot extraction frequency is within a threshold. Furthermore, the energy measuring device is configured to extract an operating state of the at least one load device at a snapshot extraction frequency. The operating condition is one of a steady state and a transient state; the device generates and transmits one of only one or representative snapshots of the power information and a plurality of snapshots according to an operating state. Furthermore, the energy measurement information system comprises a server arranged to calculate the signal correlation so as to reflect the power information of the at least one load device from the snapshot of the power signal. The snapshot of the power signal is associated with one of a voltage snapshot and a current snapshot of the waveform, the waveform having a predetermined period being measured by the remote energy measuring device. The server is configured to classify the power information into one of on-stream and off-stream according to a composition unit that composes the at least one load device according to the signal correlation. Furthermore, the server is arranged to generate a data set of at least one load device from the classified power information.

In one embodiment, the server is arranged to map and reorganize the sorted data sets according to a time domain; and labeling the recombined data set.

In one embodiment, the energy measurement device is configured to collect power information. The power information includes power signals of a plurality of load devices at the power input point.

Accordingly, embodiments herein provide a method of load balancing in an energy measurement information system. The method includes collecting power information at a snapshot extraction frequency by a power information collection unit. The snapshot extraction frequency is within a range. Also, the method includes detecting an operation state of at least one load device by an operation state extraction unit at a snapshot extraction frequency. The operating condition is one of a steady state and a transient state. In addition, the method includes generating a data set by a data set generating unit, the data set including a representative snapshot of the power information being generated when the normal state is detected; and generating a data set including a plurality of snapshots of the power information when the transient is detected.

In one embodiment, the snapshots are selected according to a metering method.

In one embodiment, the method includes transmitting, by a transmission unit, a representative snapshot of power information upon detection of a normal state; and transmitting multiple snapshots of the power information upon detecting the overload condition.

In an embodiment, the power information collecting unit is arranged to collect power information, wherein the power information comprises power signals of a plurality of load devices at the power input point.

In one embodiment, the snapshot of the power information includes one of a voltage snapshot and a current snapshot having a predetermined periodic waveform as the power information.

Accordingly, embodiments herein provide a method of load management in an energy measurement information system. The method includes calculating at the server a signal correlation to reflect power information for at least one load device based on a snapshot of a power signal associated with one of a voltage snapshot and a current snapshot of a waveform having a predetermined period as measured by a remote energy measurement device. Further, the method includes classifying, in the server, the power information according to a constituent unit that constitutes the at least one load device according to the signal correlation, the power information being classified as one of being operated and being stopped. Further, the method includes generating, at the server, a data set of at least one load device based on the classified power information.

In one embodiment, the method includes mapping and reorganizing, at the server, the sorted data sets according to a time domain; and the reassembled data set is marked at the server.

In one embodiment, the operating conditions are used to differentiate the distribution plane of each load device.

These and other aspects of the embodiments herein will be better appreciated and understood when considered in conjunction with the following description and the accompanying drawings. It should be understood, however, that the following description, while indicating preferred embodiments and numerous specific details thereof, is given by way of illustration and not of limitation. Many changes and modifications may be made within the scope of the embodiments herein without departing from the spirit thereof, and the embodiments herein include all such modifications.

Drawings

The present invention is illustrated by the accompanying figures, in which like reference numerals refer to corresponding parts throughout the several views. The embodiments herein may be better understood by reference to the following description taken in conjunction with the accompanying drawings in which:

FIG. 1 illustrates a high level overview of a system for managing power demand and load balancing according to one embodiment disclosed herein;

FIG. 2 is a flow chart illustrating a method of managing power according to one embodiment disclosed herein;

FIG. 3 is a block diagram of a server for managing power demand according to one embodiment disclosed herein;

FIG. 4 is a block diagram of a communication device for managing power requirements according to one embodiment disclosed herein;

fig. 5a and 5b illustrate screens for outputting guidance information according to one embodiment disclosed herein;

FIG. 6 illustrates a screen outputting compensation information according to one embodiment disclosed herein;

FIG. 7 is a block diagram illustrating an energy measurement device of a power input point according to one embodiment disclosed herein;

8a, 8b, and 8c are flow diagrams illustrating various operations of various components of an energy measurement device at a power input point according to one embodiment disclosed herein;

FIG. 9a is a flow chart illustrating various operations performed by an energy measurement device for load management between the energy measurement device and a server according to one embodiment disclosed herein;

FIG. 9b is a flow chart illustrating various operations performed by a server for load management between an energy measuring device and the server according to one embodiment disclosed herein;

FIG. 10 is a block diagram illustrating a tagging server according to one embodiment disclosed herein;

FIG. 11 is a flow chart illustrating various operations of a mark-up server according to one embodiment disclosed herein;

FIG. 12 is a chart illustrating a probability distribution for achieving a reduction in the amount of reduction requests based on the amount of compensation per different unit usage per user expected, according to embodiments disclosed herein;

FIG. 13a is a flow chart illustrating a method of predicting power consumption from consumption signatures, according to embodiments disclosed herein;

FIG. 13b is a flowchart illustrating various operations performed to extract power consumption elements to predict power consumption from consumption characteristics, according to embodiments disclosed herein;

FIG. 14 is a graph illustrating an example of calculating correlation coefficients according to embodiments disclosed herein;

FIG. 15 is a graph illustrating a relationship between power consumption and temperature for each feedback wire according to embodiments disclosed herein;

FIG. 16 is a graph illustrating an example of a relationship between estimated power consumption and temperature according to embodiments disclosed herein;

FIG. 17 is a chart illustrating a modeling example according to embodiments disclosed herein;

FIG. 18 is a flowchart illustrating a method of predicting power consumption in accordance with other exemplary embodiments of the invention; and

FIG. 19 is a block diagram illustrating an apparatus for predicting power consumption from consumption characteristics according to embodiments described herein.

Detailed Description

The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and described in the following description. Descriptions of well-known components and techniques are omitted so as to not unnecessarily obscure the embodiments herein. Moreover, the various embodiments described herein are not necessarily mutually exclusive, as some embodiments may be combined with one or more other embodiments to form new embodiments. The term "or" as used herein refers to a non-exclusive or unless otherwise indicated. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.

Before describing the present invention in detail, it is useful to provide definitions of the key terms and concepts employed herein. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

Request signal: the request signal for saving electricity may contain information on at least one of a reduction requirement for power consumption of electricity, a time period for which reduction is required, an area for which reduction is required, and the like. For example, the request signal may contain information indicating 10,000kWh (power consumption requiring curtailment) at 2 pm to 5 pm (time period requiring curtailment) on day 1 of month 5 in 2015. Further, the request signal may contain information specifying a specific area (e.g., water city, seoul river south area, etc.) as an area requiring curtailment.

User information: the user information described herein may include at least one item of information about power consumption or expected consumption by all or each load device per customer, probability distribution functions that all or each load device reduces by compensating for each customer, and the like. Also, the user information may include registration information about the communication device, the user load device, information about the electricity charge per user, and the like.

Guiding information: the guide information for saving electricity may include at least one of information on power consumption required to be saved per user (the user is selected to request the saving of electricity), a power usage state of the corresponding user, a period of time required to be saved, an expected compensation by the saving, and the like. The guidance information may be provided for all load devices used by the user or for a single load device.

Compensation information: the information on the compensation of the reduced electricity may include information on a discount of the electricity fee given to the corresponding user, a point corresponding to the guidance information available when the electricity fee proportional to the actually reduced electricity consumption is paid, and the like.

Embodiments herein provide a server for managing power demand. The server comprises a storage unit, a receiving unit, a selecting unit, a transmitting unit, a monitoring unit and a compensation management unit, wherein the storage unit is used for storing user information; the receiving unit is configured to receive a request signal for reducing power consumption; the selection unit is configured to select a user corresponding to the request signal according to the user information; the transmission unit is configured to transmit the power-saving guidance information to the communication device of the user; the monitoring unit is configured to monitor power consumption by at least one load device of the selected user; the compensation management unit is configured to compensate the user when the power consumption of the at least one load device is reduced according to the guidance information.

Accordingly, embodiments herein implement an energy measurement device at a power input point for load balancing in an energy measurement information system. The energy measurement device includes a power information acquisition unit configured to acquire power information at a snapshot extraction frequency. The snapshot extraction frequency is within a range. The energy measuring device comprises an operating state extraction unit which is configured to detect an operating state of the at least one load device with a snapshot extraction frequency. The operating condition is one of steady state and transient. Furthermore, the energy measuring device comprises a data set generating unit arranged to generate a data set comprising a representative snapshot of the power information upon detection of the normal state; and generating a data set comprising a plurality of snapshots of the power information when the transient is detected.

Accordingly, embodiments herein implement a server for load management in an energy measurement information system. The server comprises a controller unit arranged to calculate the signal dependency so as to reflect the power information of the at least one load device based on a snapshot of the power signal. The snapshot of the power signal is associated with one of a voltage snapshot and a current snapshot of a waveform, and the remote energy measurement device measures the waveform with a predetermined period. And the controller unit is configured to classify the power information into one of an on-stream and an off-stream according to a composition unit that composes the at least one load device according to the signal correlation. Furthermore, the controller unit is arranged to generate a data set of at least one load device based on the classified power information. In one embodiment, the controller unit is arranged to map and rebin the sorted data sets according to the time domain; and labeling the recombined data set.

Accordingly, embodiments herein implement an energy measurement information system. The energy measurement information system includes an energy measurement device configured to collect power information at a snapshot extraction frequency. The snapshot extraction frequency is within a threshold. Furthermore, the energy measuring device is configured to extract an operating state of the at least one load device at a snapshot extraction frequency. The operating condition is one of a steady state and a transient state; and a representative snapshot or snapshots of power information are generated and transmitted based on the operating state. Furthermore, the energy measurement information system comprises a server arranged to calculate the signal correlation so as to reflect the power information of the at least one load device based on the snapshot of the power signal. The snapshot of the power signal is associated with one of the voltage snapshot and the current snapshot, and the predetermined period of the waveform is measured by the remote energy measuring device. The server is configured to classify the power information into one of an on-stream and an off-stream according to a composition unit that composes the at least one load device according to the signal correlation. Furthermore, the server is arranged to generate a data set of at least one load device from the classified power information. In one embodiment, the server is arranged to map and reorganize the sorted data sets according to a time domain; and labeling the recombined data set.

Accordingly, embodiments herein implement a method of load balancing in an energy measurement information system. The method includes collecting power information at a snapshot extraction frequency by a power information collection unit. The snapshot extraction frequency is within a range. Also, the method includes detecting an operation state of at least one load device by an operation state extraction unit at a snapshot extraction frequency. The operating condition is one of steady state and transient. In addition, the method includes generating a data set by a data set generating unit, the data set including a representative snapshot of the power information being generated when the normal state is detected; and generating a data set comprising a plurality of snapshots of the power information when the transient is detected.

Accordingly, embodiments herein implement a method of load management in an energy measurement information system. The method includes calculating at the server a signal correlation to reflect power information for at least one load device based on a snapshot of a power signal associated with one of a voltage snapshot and a current snapshot of a waveform, the remote energy measurement device measuring the waveform with a predetermined period. Further, the method includes classifying, at the server, the power information according to a constituent element that constitutes the at least one load device according to the signal correlation, the power information being classified as one of operating and not operating. Further, the method includes generating, at the server, a data set of at least one load device based on the classified power information. In one embodiment, the method includes mapping and reorganizing, at the server, the sorted data sets according to a time domain; and the reassembled data set is marked at the server.

Referring now to the drawings, and more particularly to fig. 1-19, wherein like reference numerals designate corresponding features throughout the several views, there is shown a preferred embodiment.

Fig. 1 illustrates a highly schematic diagram of a system 100 for managing power demand and managing load balancing, according to one embodiment disclosed herein. In one embodiment, the system 100 for managing power demand and load balancing may include an energy measurement device 102 at a power input point, a server 104, a communication device 106, at least one load device 108 used by a user of the communication device 106, and a power supply enterprise server 110.

In one embodiment, system 100 may additionally connect an originator server 112 and a carbon dioxide emissions trading server 114.

The server 104 may have the functionality to manage power demand and load balancing between the energy measurement devices 102 of the input points and the server 104. In one embodiment, server 104 described herein is the same as tagging server 700 described in FIG. 7 (same configuration as described in FIG. 1). In this case, server 104 may further include components to perform power demand management functions as compared to branding server 700. In one embodiment, server 104 may be a server provided separately from mark-up server 700 to perform power demand management functions (and thus may have a different configuration).

The tag server 700 is not illustrated in fig. 1, but it may be assumed that the server 104 performs the functions of the tag server 700 (in the case of the same configuration), or that the system 100 further includes the tag server 700 in addition to the server 104 (in the case of a different configuration).

The communication devices 106 described herein are registered as communication devices of users by the server 104 to facilitate data communication with the server 104. Some non-limiting examples of the communication device 106 may include a mobile device, a smartphone, a laptop, a tablet, a stationary household appliance (e.g., a television, a refrigerator, an air conditioner, etc.), and so forth.

In one embodiment, energy measurement device 102 includes a power information collection unit configured to collect power information at a snapshot extraction frequency. The snapshot extraction frequency is in the range of 10 to 900 times per second. The energy measuring device 102 is configured to detect an operating condition of at least one load device at a snapshot extraction frequency. The operating conditions described herein may be one of steady state and transient state. Further, the energy measurement device 102 can be configured to generate a data set including a representative snapshot of power information upon detection of a normal state; and generating a data set comprising a plurality of snapshots of power information when the transient is detected.

Furthermore, in one embodiment, the server 104 is configured to calculate the signal correlation to reflect the power information of the at least one load device from a snapshot of the power signal. The snapshot of the power signal is associated with one of a voltage snapshot and a current snapshot of the waveform, and the waveform having a predetermined period is measured by the remote energy measuring device. Furthermore, the server 104 is configured to classify the power information into one of on-stream and off-stream according to a composition unit that composes the at least one load device according to the signal correlation. Furthermore, the server 104 is arranged to generate a data set of at least one load device based on the classified power information.

Further, the server 104 is arranged to map and reorganize the sorted data sets according to the time domain; and labeling the recombined data set.

Fig. 1 illustrates a limiting overview of one system for managing power demand and load balancing between an energy measurement device 102 and a server 104 at an input point, however, it should be understood that this is not limiting to other embodiments. The labels provided for each unit, device or component are for illustrative purposes only and do not limit the scope of the invention. Also, one or more elements, devices, or components may be combined or separated to perform similar or substantially similar functions without departing from the scope of the invention. Further, the system 100 may include other various components that interact locally or remotely along with other hardware components or software components to manage power demand and load balancing between the energy measurement devices 102 and the servers 104 at the input points. For example, a component may be, but is not limited to being, a program, an object, an executable, a thread of execution, a program, or a computer running in a controller and a processor.

Fig. 2 is a flow chart illustrating a method of managing power usage according to one embodiment disclosed herein. At S202, the method includes receiving a request signal to reduce power. In one embodiment, the method enables the server 104 to receive a request signal from the power supply enterprise server 110 to reduce power.

At S204, the method includes selecting a user from which server 104 requests a power reduction. In one embodiment, the method allows the server 104 to select a user and request a power savings by receiving a request signal with user information. The server 104 described herein may store and update user information by reflecting a power usage state or a user pattern, or update user information according to a user's request.

In one embodiment, the server 104 may select at least one user and request a power savings based on predetermined criteria that take into account power consumption, anticipated power consumption, probability distribution of power savings based on compensation, and the like. The server 104 may select the user who is expected to perform the power down request at the lowest cost. Alternatively, the server 104 may select users that are expected to have a higher probability of responding to power down requests. The details of the user part are explained in connection with fig. 3.

At S206, the method includes transmitting guidance information to save the power. In one embodiment, the method allows the server 104 to transmit guidance information regarding the curtailment of the electricity to the user' S communication device 106 selected in step (S204).

At S208, the method includes monitoring power consumption. In one embodiment, the method allows the server 104 to monitor power consumption through at least one load device of the selected user. At S210, the method includes determining whether the actual power consumption is reduced and whether the reduced power consumption in the load devices of the selected customer complies with the guidance information. In one embodiment, the method allows the server 104 to verify whether the actual power consumption is reduced and whether the reduced power consumption in the load devices of the selected users corresponds to the guidance information transmitted in step (S206). Monitoring of power consumption can be understood with reference to the above description of collecting and retrieving data on electricity usage by the marking server 700 as described in fig. 7.

At S212, the method includes granting a predetermined compensation to the corresponding user. In one embodiment, when the server 104 determines that the power consumption is reduced to be consistent with the boot information, the server 104 may give compensation corresponding to the reduction in power consumption. The server 104 may decide to compensate for the corresponding user by considering the actual reduced power consumption corresponding to the guidance information.

For example, the server 104 may compensate in light of the reduced power consumption. Also, the server 104 may compensate for the power consumption fee and give the compensation as a discount of the power consumption or give an available point for payment of the power consumption fee.

Alternatively, in one embodiment, the compensation may include cash, gifts, coupons, etc. provided by the sponsor server 112 in addition to the power supply enterprise server 110. In this case, the server 104 may receive cash, gifts, coupons, etc. from the sponsor server 112 in electronic form. Alternatively, the server 104 may electronically maintain conversion points corresponding to cash, gifts, coupons received from the sponsor in the account of the respective sponsor. The server 104 calculates the reduced power consumption corresponding to cash, gifts, coupons, or transition points received for each sponsor. Also, the originator server 112 may obtain the carbon dioxide emission right from the carbon dioxide emission right transaction server 114 according to the calculated reduced power consumption. In this context, "based on the reduced power consumption" may refer, non-exclusively, to all cases where the reduced power consumption is converted into a reduced amount of carbon dioxide based on the reduced power consumption, which the skilled person may convert into the reduced amount of carbon dioxide using conventional methods known or to be known in the art. Also, the carbon dioxide emissions right transaction server 114 may verify whether the reduced power is as much as cash, gifts, coupons, etc. that the sponsor server 112 provides from the power supply enterprise server 110 or the server 104.

Also, at S214, the method includes transmitting information regarding the compensation to the communication device 106. In one embodiment, the method allows the server 104 to be able to transmit compensation information regarding the compensation given to the communication device 106 of the respective user. The user of the communication device 106 may verify whether the actual power consumption cost is discounted by the user's power saving behavior and the extent of the discount. This has the effect of prompting the user to actively participate in the power saving when the power saving is required.

Unlike conventional systems and methods, power-down solutions based on non-intrusive load monitoring for user power usage can be implemented without having to build expensive power-down systems. Also, the reduction of the electricity usage is performed by taking into account a period of time during which the electricity usage needs to be reduced. Power savings are prompted to the user, who can perform a given power savings at the lowest cost, to increase the efficiency of power savings. The power reduction is compensated to encourage the user to actively participate in the power reduction. Furthermore, the proposed system and method can be implemented using existing infrastructure and without requiring extensive setup and equipment.

The various operations, acts, blocks, steps, etc. in fig. 2 may be performed in the order presented, in a different order, or concurrently. Moreover, in some embodiments, certain operations, acts, blocks, steps, etc. may be omitted, added, modified, skipped, etc., without departing from the scope of the present invention.

FIG. 3 is a block diagram of one server 104 for managing power demand and load balancing according to one embodiment disclosed herein. In one embodiment, the server 104 may include a storage unit 302 storing user information, a receiving unit 304 receiving a request signal for saving electricity, a transmitting unit 306 transmitting guide information about saving electricity to a communication device of a selected user, a selecting unit 308 selecting a user by receiving the request signal and using the user information and requesting the saving of electricity, a monitoring unit 310 monitoring power consumption of a load device of the selected user, and a compensation management unit 312 performing predetermined compensation for the corresponding user when the power consumption of the load device is reduced according to the guide information.

The receiving unit 304 and the transmitting unit 306 may be implemented as a plurality of separate units or one communication unit 314. Furthermore, the selection unit 308, the monitoring unit 310 and the compensation management unit 312 may be implemented as a plurality of separate units or one control unit 316.

The receiving unit 304 and the transmitting unit 306 may be implemented as a plurality of separate modules or one communication module 314. Furthermore, the selection unit 308, the monitoring unit 310 and the compensation management unit 312 may be implemented as a plurality of separate modules or one control module 316.

The storage unit 302 may store user information, in particular, information about power usage by each user. For example, the storage unit 302 may store information on power consumption of all load devices or a single load device per user, a temporary power profile, a history of saving power according to the guide information, and the like as user information. More specifically, the history of saving power according to the guidance information, which includes the compensation method and the compensation amount, may include information on whether or not the power consumption is actually reduced, the reduced power consumption, the load device that reduces the power consumption, and the like, corresponding to the previous guidance information.

The receiving unit 304 may receive a request signal for reducing power consumption from the power supply enterprise server 110 before the start of a period of time in which the reduction of power is required.

For example, the power supply enterprise server 110 may transmit the request signal a week, a day, or an hour before the start of the period of time in which the power saving is required. This method causes an active power-down behavior by providing guidance information on power-down to the user before the time period in which power-down is requested.

The selection unit 308 may select at least one user to which the server 104 requests a power saving according to a predetermined criterion that takes into account power consumption, temporary power consumption, a probability distribution of power saving according to compensation, and the like. Thus, the server 104 may select a user who is expected to perform a power down request at the lowest cost. Alternatively, the server 104 may select a user with a higher probability of anticipating a response to the power down request.

For example, the selection unit 308 may select at least one user so as to save electricity at the lowest cost by considering the degree of power consumption of the period of time in which the saving is required, the predicted value of power consumption of the period of time in which the saving is required, the reaction rate of the current saved electricity or the saved power consumption, the probability distribution of the saving according to the compensation, and the like. In one embodiment, the probability distribution of the curtailment according to the compensation may be estimated based on the actual curtailment of power consumption with respect to the last curtailment power request. Furthermore, the user changing the usage according to the accumulation step in order to change the cost can also be used to generate a probability distribution that is reduced by the compensation. The probability distribution of achieving the curtailment target based on the compensation may vary based on the expected power consumption of each load device or all load devices at the curtailment target time. An example chart illustrating a probability distribution for achieving a reduction in the reduction request amount based on the consumption amount per user expected per different unit usage is described in connection with fig. 12.

The selection unit 308 calculates a unit compensation amount of power consumption satisfying the required reduction at the lowest cost and a reduction request amount Δ i for each individual user (i ═ 1, … … n). The compensation amount is calculated in consideration of the probability distribution Fi (Δ; p) that is reduced according to the compensation, and the probability distribution of the compensation amount p per unit usage included in the request signal and the request power consumption W that is reduced is configured for each individual user.

Finding Deltai, p

Make it

Minimum size

So that

Here, a probability distribution function of the curtailment is estimated for each individual device to calculate a curtailment request amount for each individual device. Alternatively, the selection unit 308 may select the users so that the expected value of the reaction rate of each user or the load device of each user and the history of the curtailment use satisfy the power consumption required to be curtailed included in the request signal.

Also, the selection unit 308 may select the user by calculating an expected power consumption requiring a reduction by more than a predetermined amount than the power consumption requiring a reduction included in the request signal. Thereby prohibiting the occurrence of users not participating in power saving among the selected users.

For example, the selection unit 308 may select the user so that the total amount of expected power consumption requiring curtailment becomes 20,000kWh by considering the power consumption at 2 pm, the reaction rate, and the curtailment amount according to the previous guidance information when receiving the request signal (request for curtailment of 10,000kW at 2 pm on the tomorrow). In this case, the selection unit 308 calculates the power consumption required to be reduced, which is included in the two request signals, as the expected power consumption required to be reduced, so as to select the user based on the calculated expected power consumption required to be reduced when the power saving reaction rate of the present guidance information is 50%.

Moreover, the selection unit 308 may select users of the server 104 who request a power reduction, excluding users who normally use the base power source during the time period in which the reduction is requested. Here, the base power source may refer to a backup power source of the load device or the lowest power that maintains the active state. When the possibility of power saving of the load devices using only the base power supply is very low, the selection unit 308 may exclude the corresponding load devices from the selected power saving target.

For example, the selection unit 308 calculates a power value obtained by subtracting the base power consumption from the expected power consumption in a time period. This requires that the electricity be saved for all consumers and the calculated power value be determined in accordance with the saved power consumption to calculate the expected required saved power consumption and the required saved power consumption for each consumer requiring the saved power and each consumer within the reducible power consumption.

Also, when the selected user uses a plurality of load devices, the selection unit 308 may decide the total amount of power consumption that the selected user needs to reduce and the power consumption that is required to be reduced for each load device or each load device that can save power.

In addition, when information on a designated area where power saving is required is included in the request signal, the selection unit 308 may select one user from among users residing in the designated area, to which the server 104 requests power saving.

The transmission unit 306 may transmit guidance information about saving the electricity to the communication apparatus 106 of the user selected by the selection unit 308.

For example, the guide information may contain a period of time for which power saving is required, power consumption for which all load devices or a single load device can be reduced, guide information for saving power (e.g., lowering the temperature of an air conditioner to a predetermined temperature, increasing the freezing or cooling temperature of a refrigerator, unplugging unused electronic products from a socket, etc.), compensation information depending on the saved power, etc.

After transmitting the guidance information, the monitoring unit 310 may monitor power consumption through the load device of the selected user. It is thus determined whether the power consumption of the load device of the user is actually reduced or whether the degree of reduction of the power consumption conforms to the guidance information and compensation is given.

When the power consumption of the load device of the selected customer is reduced according to the transmitted guidance information, the compensation management unit 312 performs a predetermined compensation for the corresponding customer, the compensation being proportional to the reduced power consumption. In more detail, the compensation management unit 312 may give compensation for the power consumption rate.

For example, the compensation management unit 312 may give compensation by discounting power consumption fees or providing points available when the power consumption fees are paid. Alternatively, the compensation management unit 312 may compensate for cash, gifts, coupons, etc. received from the sponsor server 112.

Further, the transmission unit 306 may transmit information on power saving according to the guidance information of each user (hereinafter, simply referred to as saving information) to the power supply enterprise server 110. The power supply server 110 may decide compensation according to whether the power is reduced and the reduced power consumption included in the reduction information, and transmit information on the decided compensation to the server 104. Accordingly, the compensation management unit 312 may give compensation to the corresponding user according to the information on the compensation received from the power supply-related server 110.

Meanwhile, the compensation management unit 312 may determine a criterion of giving compensation in advance according to whether power is reduced and power consumption reduced according to the guidance information, and give compensation to a corresponding user according to the criterion of giving compensation.

The transmission unit 306 may transmit information regarding the given compensation (hereinafter referred to as compensation information) to the communication device 106 of the corresponding user.

Furthermore, in an embodiment, the controller unit 316 is arranged to calculate the signal correlation to reflect the power information of the at least one load device in dependence on a snapshot of the power signal, wherein said snapshot of the power signal is related to one snapshot of a voltage snapshot and a current snapshot of a waveform, said waveform having a predetermined period being measured by the remote energy measuring device. Furthermore, the controller unit 316 is arranged to classify the power information according to a composition unit which composes the at least one load device according to the signal dependency, wherein the power information is classified as one of being operated and being out of operation. Furthermore, the controller unit 316 is arranged to generate a data set of at least one load device based on the classified power information. Furthermore, the controller unit 316 is arranged to map and rebind the sorted data sets according to the time domain; and labeling the recombined data set.

FIG. 3 illustrates a limiting overview of the server 104 for managing the basis of power demand, however, it should be understood that this is not limiting to other embodiments. The labels provided for each unit or module are for illustrative purposes only and do not limit the scope of the invention. Also, one or more units or modules may be combined or separated to perform similar or substantially similar functions without departing from the scope of the invention. Further, the server 104 may include other various components that interact with other hardware components or software components, either locally or remotely, to manage power requirements.

Fig. 4 is a block diagram of a communication device 106 for managing power requirements according to one embodiment disclosed herein. In one embodiment, the communication device 106 may include a communication unit 402 that receives boot information regarding the reduction of power from the server 104, a control unit 404 that executes a power-related application by receiving the boot information, and an output unit 406 that outputs the boot information by executing the power-related application. Also, the communication device 106 may further include a storage unit 410 storing power-related applications, guidance information, compensation information, and the like.

The control unit 404 may control the operation of at least one of the communication unit 402, the output unit 406, the input unit 408, and the storage unit 408. The power-related application is an application that is handled by the user of the communication device 106 and manages the power consumption of the load device, and may be installed while the communication device 106 is produced or may be downloaded from an external server by the user's selection.

The control unit 404 may automatically execute the power-related application or may execute the power-related application only when a notification of receiving the guidance information is provided to the user and then an execution instruction is received from the user and when the guidance information is received.

For example, the notification of receiving the guidance information may indicate brief contents of the guidance information, and output a vibration/light/sound of the notification only to notify whether or not the guidance information is received. The output of the guidance information will be described with reference to fig. 5a and 5 b.

Also, the communication unit 402 may receive information on compensation (hereinafter, simply referred to as compensation information) given for the curtailed electricity corresponding to the guidance information from the server 104, and the output unit 406 executes the power-related application to output the received compensation information. The output of the compensation information is explained with reference to fig. 6.

Since the reception notification of the compensation information can be described by referring to the description of the reception notification of the guidance information, a detailed description thereof is omitted for the sake of brevity.

Also, the communication device 106 may further include an input unit 408 that receives a power saving instruction with respect to at least one load device by executing the power-related application. The communication unit 402 may transmit a power-saving instruction signal corresponding to the power-saving instruction to the load device 108.

For example, the communication device 106 may receive an instruction or selection related to operation control (e.g., power on/off, temperature control of an air conditioner/refrigerator, etc.) of each load device from a user through the input unit 408. In more detail, the input unit 408 may receive a selection of load devices intended to save power, or a method of saving power of each load device, from a user.

The load device 108 may reduce power consumption corresponding to the power-saving command signal when receiving the power-saving command signal. In one embodiment, the load device 108 may respond to the communication device 106 as to whether or not to perform a power-saving operation corresponding to the command signal for power saving (hereinafter simply referred to as a response signal).

For example, when the load device 108 is a refrigerator and the command signal for saving power includes a recommended frozen or chilled room temperature for a predetermined time, the recommended frozen or chilled room temperature may be maintained for the predetermined time. Alternatively, when the load device 108 is a computer and the power saving command signal includes a command to turn off the power, the power of the computer may be turned off.

Fig. 5a and 5b illustrate a limiting overview of the communication device 106 managing the basis of power requirements, however, it should be understood that this is not limiting to other embodiments. The labels provided for each element are for illustrative purposes only and do not limit the scope of the invention. Also, one or more elements may be combined or separated to perform similar or substantially similar functions without departing from the scope of the invention. Further, the communication device 106 may include other various components that interact with other hardware components or software components, either locally or remotely, to manage power requirements.

Fig. 5a and 5b illustrate screens for outputting guidance information on power consumption according to an embodiment of the present invention. Here, it is assumed that the guidance information may be output when the power-related application is executed.

Fig. 5a and 5b illustrate screens of outputting guidance information according to one embodiment disclosed herein. According to fig. 5a, the communication device 106 may display a screen including a time period (2: 30 pm to 3:00 pm) for which power saving is required and expected compensation information (5,000 point reduction amount saved) according to the power saving amount as the guide information.

According to fig. 5b, the communication device 106 displays a user's guide information implementing a status list based on the power consumption savings and compensation amount, thus encouraging the user to participate more actively in the power consumption savings.

FIG. 6 illustrates a screen that outputs compensation information regarding curtailment of electricity according to one embodiment disclosed herein. It is assumed that the compensation information can be output by executing the power-related application. According to fig. 6, when the communication device 106 obtains a point (hereinafter, simply referred to as a compensation point) that can be used to pay the power consumption fee to correspond to the reduction of power consumption, the communication device 106 may display the obtained compensation point (for example, 3200 points) and show a method by which the obtained compensation point can be redeemed. In addition, the communication device 106 may display the total amount obtained by converting the compensation points into cash (e.g., 777,777,777 circles) and a ranking of the users of the communication device 106 among all users (e.g., 37 th).

Referring to fig. 7-10 hereinafter, an energy measurement device at an input point and a marking server 700 that generates power information by marking a data set received from the power information are described according to an embodiment of the present invention.

Fig. 7 is a block diagram illustrating an energy measurement device 102 for a power input point, according to one embodiment disclosed herein. In this embodiment, the energy measurement device 102 is configured to generate an unregistered load aggregation data set to estimate energy consumption information of each load device connected to the power input point, respectively, and transmit the estimated energy consumption information to the energy measurement information tag server 700.

The energy measuring device 102 described herein is mounted with a single sensor at the power input point. The energy measurement device 102 performs a series of operations to measure the total power consumption and estimate the energy consumption of each load device. Unlike conventional systems and methods, the following summarizes the previous information processing procedures implemented for each load device.

First, a snapshot is taken from the voltage or current signal. Noise filtering is performed by extracting reference points. And distinguishing the normal state or the overload state, the active power, the reactive power and the like of the voltage according to the corresponding result and the operation state. The operating state change such as a switching event of a single load device is extracted by distinguishing a normal state or an overload state. In addition, a final aggregated data set is generated by matching load classification with load characteristics, and the load classification is performed by voltage-current correlation, high-frequency distortion, current or power snapshot signal deformation, correlation of active power or reactive power, and the like. Further, the generated aggregated data set is transmitted to the energy measurement information tag server 700 or the cloud through data compression of the unregistered state. For example, a load classification flag such as 1, 2, 3, or A, B, C may not be registered and may not be recognizable to the user.

The energy measurement device 102 may include a power information acquisition unit 702, an operating state extraction unit 704, a data set generation unit 706, and a transmission unit 708.

In one embodiment, the power information collection unit 702 is configured to collect energy information or power information including power signals of a plurality of load devices at a power input point. The load device described herein may include an energy-consuming device or component that uses electrical energy. In one embodiment, the load device includes both a single energy device such as a television, a refrigerator, and the like, and a constituent unit such as an engine, a lamp, and the like. For example, the power input point may be a node to which power is input with respect to a plurality of load devices, such as a power input point of a distribution board or a household distribution board. Further, various operations performed by the power information collecting unit 702 will be described in detail with reference to fig. 8 a.

In one embodiment, the operation state extraction unit 704 is configured to distinguish a normal state or an overload state of a power change by the collected voltage information or power information, thereby extracting a change pattern of the operation state or the operation state of the load device. Also, various operations performed by the operation state extraction unit 704 are described in detail with reference to fig. 8 b.

In an embodiment, the data set generation unit 706 is arranged to generate one data set for each individual load device, matching the operating state or the change pattern of the operating state by signal correlation in dependence of the individual load device power usage information. Various operations performed by the operation data set generation unit 706 are described in detail in conjunction with fig. 8 c.

When generating the data set, the transmission unit 708 may be arranged to transmit the generated data set to the energy measurement information tag server 700, which generates tagged power information by reassembling the data set.

In one embodiment, an energy measurement device 102 at a power input point for load balancing between the energy measurement device 102 and the server 104 is described. The power information acquisition unit 702 is arranged to acquire power information at a snapshot extraction frequency. The snapshot extraction frequency described herein is in the range of 10 to 900 times per second. The operating state extraction unit 704 is arranged to detect the operating state of at least one load device at a snapshot extraction frequency. The operating condition described herein is one of steady state and transient state. Furthermore, the data set generating unit 706 is arranged to generate one representative snapshot of the power information when a normal state is detected. The data set generation unit 706 is arranged to generate a plurality of snapshots of the power information when a transient is detected. Further, the transmission unit 708 is arranged to transmit a representative snapshot of the power information upon detection of the normal state; when a transient is detected, a snapshot of all power information is transmitted. Further, operations performed for load management between the server 104 and the energy measuring device 102 are described with reference to fig. 9a and 9 b.

Fig. 7 illustrates a limiting overview of the energy measurement device 102, however, it should be understood that this is not limiting to other embodiments. The labels provided for each unit or element are for illustrative purposes only and do not limit the scope of the invention. Moreover, one or more modules may be combined or separated to perform similar or substantially similar functions without departing from the scope of the invention. Further, energy measurement device 102 may include various other components that interact locally or remotely with other hardware components or software components to measure energy usage information for a plurality of load devices connected to the power input point. For example, a component may be, but is not limited to being, a program, an object, an executable, a thread of execution, a program, or a computer running in a controller and a processor.

Fig. 8a is a flow chart illustrating various operations performed by the power information collection unit 702 of the energy measurement device 102 at a power input point according to embodiments described herein. In this embodiment, the power information collecting unit 702 may be configured to measure the power signal (step S802). The raw power information waveform of the current or voltage can be measured by the energy measuring device 102 and a single sensor installed at the power input point.

Also, the power information collecting unit 702 may be configured to extract a snapshot (step S804). A voltage snapshot or a current snapshot of an alternating current waveform having a predetermined period is collected. In this embodiment, it is preferable to extract a voltage snapshot having an alternating periodic waveform and a high-frequency current.

Fig. 8b is a flow chart illustrating various operations performed by the operational state extraction unit 704 of the energy measurement device 102 at the power input point according to embodiments described herein. The operation state extraction unit 704 is configured to distinguish a normal state or an overload state of a power change by the collected voltage information or power information to extract an operation state or a change pattern of the operation state of the load device.

Referring to fig. 8b, the operation state extraction unit 704 may be configured to extract power information and a reference point (step S806). In one embodiment, real-time power consumption and power quality information is extracted, and a reference point for distinguishing a normal state or an overload state is extracted.

In this embodiment, the reference point is preferably power consumption that is continuously used without fluctuation, and each load device is not turned on or off and is continuously turned on during the extraction of the real-time power consumption and power quality information.

Also, the operation state extraction unit 704 may be configured to separate overload responses (step S808). In one embodiment, an interval of an overload state is extracted, in which turning on or off is performed, or an operation state is changed by an operation of a single load device in power consumption.

Further, in one embodiment, the operation state extraction unit 704 may be configured to eliminate noise (step S810). Meaningless high-frequency noise generated during power consumption measurement of the total power consumption is eliminated.

Further, the operation state extraction unit 704 may be configured to classify the snapshot according to the extracted operation state or the change pattern of the operation state. For example, in the case where it is determined to be an overload response operation, the snapshot extraction frequency of the snapshot may be even higher than that of the normal state.

Further, the operation state extraction unit 704 may be configured to detect a switching event (step S812). In one embodiment, prior to aggregating each individual load device by detecting a switching event, a snapshot of the event is classified for each switching state. The operation state extraction unit 704 may be configured to detect a state change (step S814). A number of steps are provided in addition to being run or being shut down. The changing pattern of the operating state of the load having the continuously changing characteristic is detected and classified.

After detecting the state change, the operation state extraction unit 704 may be configured to process the real-time total power consumption data (step S816). In one embodiment, the power information data is manipulated and stored, and a transmission data packet is generated for total energy consumption and power quality information for the real-time power consumption service.

Fig. 8c is a flow chart illustrating various operations performed by the data set generation unit 706 of the energy measurement device 102 at the power input point according to embodiments described herein. The data set generating unit 706 may be arranged to generate one data set for each individual load device which matches the operating state or the change pattern of the operating state by means of a signal correlation depending on the power usage information of the individual load device.

Referring to fig. 8c, the data set generating unit 706 extracts the features of the load (step S820). In this embodiment, the signal correlations are generated using snapshots, overload responses, switching events, and state change information extracted from the total power consumption data, thereby reflecting the power usage characteristics of the individual load devices. The signal correlation may include voltage or current correlation, high frequency distortion, current or power signal distortion, active or reactive power correlation, and the like.

Also, the data set generating unit 706 sets to match the switching events (step S822), and classifies the load of the matching pattern (step S824) to generate a data set. The open or shut down events of the individual load devices are classified in a pair of identical load devices according to the generated signal correlations. The multi-step operation or the continuous change operation is classified into the association group by the on-going or off-going event with respect to the same load device according to the correlation of the generated signals.

Further, the data set generating unit 706 may be configured to generate one data set (step S826). And generating a data set collected according to the association group through switching event matching and pattern matching load classification.

When generating the data set, the transmission unit 708 may be configured to transmit the generated data set to the energy measurement information tagging server 700, which generates tagged power information by reassembling the data set.

In this embodiment, the data packets generated by the energy measurement device 102 are compressed prior to transmission to facilitate transfer of the mass data to the energy measurement information tagging server 700.

Also, power consumption and quality information data required to implement the real-time power information service may be transmitted together.

Also, referring to fig. 8a to 8c, the snapshot extraction (i.e., power signal sampling) period and the resulting information processing efficiency of the present invention will be described in detail.

In one embodiment, it is important that the power information acquisition unit 702 properly select the snapshot extraction frequency. The resolution of the transient intervals of the load device is lower when the snapshot extraction frequency is lower than a certain value, for example, when the snapshot extraction frequency is lower than once per second. As a result, it is difficult to distinguish between different individual load devices. When the snapshot extraction frequency is higher than a certain value, for example, when the snapshot extraction frequency is higher than thousands to ten thousand times per second, the resolution of the transient interval is too high. As a result, errors may occur, such as identifying the same load device as a different load device. Thus, a suitable snapshot extraction frequency for efficient a priori information processing of the energy measurement device at the power input point is 10 to 900 times per second.

Also, by the snapshot classification by the operating state extracting unit 704, information processing after extracting the operating state can be efficient (for example, in the snapshot extracting step (S804), a method of continuously extracting snapshots at a frequency of 15 times per second. In other words, by a method in which the data traffic-related burden is reduced as the resolution of the transient interval (required for energy usage information analysis) per device increases (for example, even in the case where the transmission unit 708 periodically transmits data once per second, when the operation state does not change, only one snapshot selected and classified is transmitted or the calculated representative value is determined by division). The load balancing capability of the overall system between the energy measurement device 102 and the server 104 is improved. Accordingly, the switching event detecting step (S812), the state change detecting step (S814), and some or all of the steps performed by the data set generating unit 706 may be performed by the server 104.

Details of an energy measurement information tagging server 700 that generates tagged power information by receiving a data set generated by an energy measurement device 102 of a power input point are described in connection with fig. 10.

The various operations, acts, blocks, steps, etc. in fig. 8 a-8 c may be performed in the order presented, in a different order, or simultaneously. Moreover, in some embodiments, certain operations, acts, blocks, steps, etc. may be omitted, added, modified, skipped, etc., without departing from the scope of the present invention.

Fig. 9a is a flow chart illustrating various operations performed by the energy measurement device 102 for load management between the energy measurement device 102 and the server 104 according to one embodiment disclosed herein. At S902, the method includes collecting power information at a snapshot extraction frequency, wherein the snapshot extraction frequency is within a range. In one embodiment, the range described herein is between 10 and 900 times per second. Unlike conventional systems and methods, an appropriate snapshot extraction frequency is selected for efficient a priori information processing of the energy measurement device of the power input point. For example, when the snapshot extraction frequency is lower than a certain value, such as when the snapshot extraction frequency is lower than once per second, the resolution of the transient interval of the load device is lower. As a result, it is difficult to distinguish between different individual load devices. When the snapshot extraction frequency is higher than a certain value, for example, when the snapshot extraction frequency is higher than thousands to ten thousand times per second, the resolution of the transient interval is too high. As a result, errors may occur, such as identifying the same load device as a different load device. Thus, a suitable snapshot extraction frequency for efficient a priori information processing of the energy measurement device at the power input point is 10 to 900 times per second.

At S904, the method includes detecting an operating state of the load device at a snapshot extraction frequency. In one embodiment, the operating condition described herein is one of a steady state and a transient state. At S906, the method includes generating a data set including a representative snapshot of power information upon detection of a normal state; and upon detection of the transient, generating a data set comprising a plurality of snapshots of the power information, as shown at S908. For example, snapshots are taken continuously at a frequency of 15 times per second. However, when the operation state is not changed, only one snapshot or one representative value among the 15 snapshots is selected for classification. When a change in operating conditions is detected, all 15 snapshots are selected to respectively increase the unique resolution of the transient interval). Unlike conventional systems and methods, snapshots are selected according to the assay method. In other words, by a method in which the data traffic-related burden is reduced as the resolution of the transient interval (required for energy usage information analysis) per device increases (for example, even in the case where the transmission unit 708 periodically transmits data once per second, when the operation state does not change, only one snapshot selected and classified is transmitted or a representative value calculated by division is determined). The load balancing capability of the overall system between the energy measurement device 102 and the server 104 is improved. As a result, the switching event detecting step (S812), the state change detecting step (S814), and some or all of the steps performed by the data set generating unit 706 may be performed by the server 104 described in fig. 9 b.

Fig. 9b is a flow diagram illustrating various operations performed by the server 104 for load management between the energy measurement device 102 and the server 104, according to one embodiment disclosed herein. At S910, the method includes calculating a signal correlation to reflect power information of the load device from a snapshot of the power signal. In one embodiment, the method allows the server 104 to calculate the signal correlations to reflect the power information of the load devices from a snapshot of the power signal. The signal correlations described herein include at least one of voltage correlations, current correlations, high frequency distortions, power signal distortions, active power correlations, and reactive power correlations. The snapshot of the power signal described herein is associated with one of a voltage snapshot and a current snapshot of the waveform, the waveform having a predetermined period being measured by the remote energy measuring device.

At S912, the method includes classifying the power information according to constituent elements that constitute the device 104 according to signal correlations. In one embodiment, the method enables the server 104 to match switching events and classify loads matching patterns to generate a data set. The open or shut down events of the individual load devices are classified in a pair of identical load devices according to the generated signal correlations. The multi-step operation or the continuous change operation is classified into the association group by the on-going or off-going event with respect to the same load device according to the correlation of the generated signals.

At S914, the method includes generating a data set for each device from the classified power information. In one embodiment, the data sets collected by the association group are generated by a load classification of switching event matching and a load classification of matching pattern.

At S916, the method may include detecting an operational state of the load device at snapshot extraction. The method enables the server 104 to detect the operational state of the load device at the time of snapshot extraction. The distribution planes are distinguished according to the load operation characteristics (on or off, multi-step, continuously changing, always active, etc.) of the individual load devices determined to be the same energy load device.

At S918, the method includes mapping and reorganizing the data set according to the time domain. In one embodiment, the method enables the server 104 to map and reorganize the sorted data sets according to the time domain. At S920, the method includes labeling the recombined data set.

The various operations, acts, blocks, steps, etc. in fig. 9a and 9b may be performed in the order presented, in a different order, or concurrently. For example, the step of generating the data set at S914 may include the steps of mapping and reorganizing the sorted data set at S918 and marking the reorganized data set at S920. Moreover, in some embodiments, certain operations, acts, blocks, steps, etc. may be omitted, added, modified, skipped, etc., without departing from the scope of the present invention.

Fig. 10 is a block diagram illustrating an energy measurement information tagging server 700 according to embodiments described herein. In one embodiment, the energy measurement information tagging server 700 may process energy usage information and process energy saving advice advisories for power consumers of the power input points through processes such as machine operation and automatic tagging based on the received aggregated data set and the real-time power consumption and power quality information data set. The energy measurement information tagging server 700 may be a mass data processing device that processes the total energy information as well as the energy information of each individual load device to produce different energy saving schemes.

In one embodiment, the energy measurement information tag server 700 may be configured to process specific post-position information through different computer operations. The process classifies unregistered load aggregation datasets into multidimensional planes according to a reference domain (referrenearea) such as active power, reactive power, time, etc. The process sets up a classification interface in the same load device through machine operations to distinguish unregistered load aggregation data sets for each particular operation or component, such as on or off, multi-step, continuously changing, always active, etc.

The differentiated data sets are mapped to real-time power consumption changes to complete the differentiation, and the lower elements (lower components) of the individual load devices are sorted into the same load devices (1+2+3 or A + B + C) that can be identified by the user. Also, the registered data sets (refrigerator, washing machine, air conditioner, etc.) of the individual load devices are matched and stored and automatically tagged.

In this case, some load devices are not automatically marked due to the data present in the registered data set, which can be manually marked by manually turning on or off the load devices that are not automatically marked and checking the corresponding time. In addition, the manually generated data is added again to the pre-acquired data set and then used for automatic labeling. Further, the respective elements of the energy measurement information tagging server 700 and the operation thereof will be described with reference to fig. 11.

Referring to fig. 10, in one embodiment, an energy measurement information tag server 700 may include a receiving unit 1002, a reorganizing unit 1004, and a tagging unit 1006.

The receiving unit 1002 can be arranged to receive a data set generated by classifying power information based on individual load devices. The reorganizing unit 1004 can be configured to sort the received data sets on a multidimensional plane based on operational characteristics of the individual load devices. Furthermore, the reorganizing unit 1004 can be arranged to reorganize the sorted data sets according to a time domain mapping and reorganization.

Before this, the reassembly unit 1004 can be arranged to decompress the data. When energy measurement device 102 transmits compressed data, energy measurement device 102 may cancel the data compression in order to increase execution speed. When the compression is removed, the reassembly unit 1004 is arranged to map the sorted data into power consumption variations in the time domain in order to reassemble elements in the same load device.

Fig. 10 illustrates a limiting overview of an energy measurement information tagging server 700, however, it should be understood that this is not limiting to other embodiments. The labels provided for each unit or element are for illustrative purposes only and do not limit the scope of the invention. Moreover, one or more components may be combined or separated to perform similar or substantially similar functions without departing from the scope of the invention. Further, the energy measurement information tagging server 700 may include various other components that interact locally or remotely with other hardware components or software components to tag extracted energy usage information for a plurality of load devices connected to a power input point. For example, a component may be, but is not limited to being, a program, an object, an executable, a thread of execution, a program, or a computer running in a controller and a processor.

Fig. 11 is a flow chart illustrating various operations performed by the energy measurement information tagging server 700 according to embodiments described herein. In one embodiment, the reassembly unit 1004 can be configured to decompress data (step S1102). When energy measurement device 102 transmits compressed data, energy measurement device 102 may cancel the data compression in order to increase execution speed. The reorganizing unit 1004 can be configured to classify the large category load devices (step S1104). The distribution planes are distinguished according to the load operating characteristics (on or off, multi-step, continuously varying, and always active) of the individual load devices determined to be the same energy load device.

Also, the reorganizing unit 1004 can be configured to perform clustering of features (step S1106). The multidimensional plane is reconfigured to facilitate setting boundaries within the distribution plane by interlocking the aggregated data sets. In one embodiment, active power, reactive power, time, etc. may be used as reference fields for reconfiguring the multidimensional plane.

When reconfiguring the multidimensional plane, the reorganization unit 1004 can be configured to perform machine learning (step S1108). The operation of generating a single load device or inter-element boundary classification reference by employing the aggregated results of each load device and by a machine operation method based on a state discrimination technique such as an artificial intelligence network. In addition, the restructuring unit 1004 may be configured to set a specific load device classification boundary (step S1110). Using a boundary classification reference of machine operation, data is classified by performing load differentiation at the individual element level of the aggregated data. In this case, the detailed load classification of the unregistered scheme is determined from the total power to the component level of the individual load device.

Also, the reassembly unit 1004 can be arranged to map the time domain (step S1112). The data set of unregistered components classified in the process is mapped to real-time data in the time domain. The reorganizing unit 1004 may be configured to distinguish the mapped data (step S1114). The mapped data is distinguished at the component level by different colors or user-recognizable presentation methods.

Further, the restructuring unit 1004 can be configured to restructure the same load (step S1116). A user-identifiable group of load devices is generated by combining the sub-elements of the individual load devices generated in the distinguishing step. As an example, the compressor, motor, lamp and control circuit characteristics generated in the distinguishing step may be combined and grouped into refrigerator groups.

After the rebinning step, a marking unit 1006 can be arranged to mark the rebinned data set. For example, the name of the respective load device automatically matches unregistered temporal tag data that is categorized as a single load device associated with the pre-stored load device data set. A, B, C, etc. may automatically register as a refrigerator, television, washing machine, etc. by data pattern and with techniques that match stored data, as one example.

Also, in this embodiment, the indicia may be received manually. Despite performing the automatic tagging, the developer or user manually names and inputs the name of the device with the associated load, which is not registered due to a mismatch with the pre-stored load device data. Methods that utilize the on/off time of the device may also be employed.

Furthermore, the respective data are stored separately while registering with respect to the individual load devices, wherein manual tagging is performed to expand the pre-stored load device data set.

Further, the energy measurement information tagging server 700 may provide data analysis information that is information of energy usage information using a single load device. Data analysis based on behavioral psychology analysis techniques can be applied to the total power and energy usage patterns of individual load devices to generate specific data sets.

Also, through data analysis, expert advice that prompts the user to save energy may be automatically generated.

In addition, a comprehensive service is available that provides total power, usage of individual load devices, energy saving counseling, etc. to specific buildings and unit residents through energy IT professional providers.

When a change in the accumulated data set differentiated at the component level is detected in relation to the state of the individual load device in order to determine the component aging state or the fault state of the individual load device, various instances of energy saving advisory may provide the user with the determined component aging state or fault state.

According to the described embodiment, the hardware of the instrument and the software technology of the server are combined in order to extract the energy usage information about the individual elements of the individual load devices from the total energy usage information of the power input point.

Moreover, because the software technology of the server is flexibly combined with a single energy measuring device, the multiple devices that obtain the high-end energy-saving scheme can extract detailed and accurate energy usage information of a single load device, and the system installation does not require high cost. In particular, energy usage information above the branch circuit level can be obtained without the need to employ multiple sensors in the electrical panel.

In summary, in the present invention, not all techniques are performed by a particular server in extracting energy usage information for individual load devices from the total power consumption measured at the power input point. Unlike the general mechanism, a priori information processing is performed so as to have a resolution distinguishable from each element in a single energy measuring apparatus, and the server collectively performs data storage, pattern analysis, and data utilization as its advantages to ensure stability of large data processing, storage, or management of respective loads related to energy use.

The various operations, acts, blocks, steps, etc. in fig. 11 may be performed in the order presented, in a different order, or concurrently. Moreover, in some embodiments, certain operations, acts, blocks, steps, etc. may be omitted, added, modified, skipped, etc., without departing from the scope of the present invention.

For example, fig. 12 is a graph illustrating a probability distribution of compensation per different unit usage per expected user to achieve a reduction in the reduction request amount according to embodiments disclosed herein. In fig. 12, the horizontal axis represents the reduction request amount, and the vertical axis represents the probability of achieving the reduction request amount. The different curves shown in fig. 12 represent the amount of compensation per unit dose. The compensation amount per unit usage may vary according to the reduction request time because the price of power generation differs due to weather conditions, power retention rate, and the like.

Fig. 13a is a flow chart illustrating a method of predicting power consumption based on consumption characteristics according to embodiments described herein. In one embodiment, according to an exemplary embodiment, a method of predicting power consumption includes a power consumption element extraction step (S1310), a relational model generation step (S1320), and a power consumption calculation step (S1330).

In the power consumption element extraction step (S1310), the power consumption of each device is segmented by time to extract at least one power consumption element that affects the power consumption. For example, the device described here can be a power supply (feeder) or a power consumer, the power consumed by which is time-sliced.

In one embodiment, the feeder supplying each electronic device is disposed below the power input point as shown in FIG. 2 for supplying power. Generally, in many cases, the purpose of use of the appliance is segmented by time for each feeder. Also, the same-purpose electric appliances are connected to one feeder line. For example, air conditioners, indoor lights, office heaters, etc. may be connected to different power supply lines for use.

In this case, in one embodiment, the power consumption may be that of a single household appliance. In addition to the power consumption measured directly at the main power input point or the lower feeder, the power consumption can be estimated indirectly from the total power consumption.

A household appliance is a combination of specific components required to operate the device and the power consumption characteristics of the individual household appliances. Because each particular component has unique energy consumption characteristics, the energy consumption characteristics based on the operating mode of the household appliance also have unique attributes. Thus, in one embodiment, the energy consumption characteristic is detected from information about the directly measured energy consumption. Also, the power consumption characteristics are compared with consumption characteristic information unique to the home appliance so as to indirectly extract the operation mode and power consumption of the individual device.

When the user's objectives for using the device are similar, the consumption patterns are correspondingly similar. As a result, unlike the general system and method, real-time consumption of each device (such as a feeder and a power-consuming device) is performed.

In other words, in the present invention, unlike the prediction of energy consumption from only the energy consumption of the input point as shown in fig. 1, the acquisition unit of the energy consumption data is miniaturized according to the home appliance or the feeder. The predictive elements for each appliance or feeder are automated and collected, and then a different predictive model is applied to each appliance or feeder. In addition, the values predicted by the individual household appliances or feeders are added up to calculate an overall predicted value.

Fig. 13b is a flow chart illustrating various operations performed to extract power consumption elements to predict power consumption from consumption characteristics according to embodiments described herein. In one embodiment, the power consumption element extracting step (S1310) includes a power consumption segmenting step (S1312), a power consumption influencing element extracting step (S1314), and a threshold or more power consumption element determining step (S1316).

In one embodiment, in the power consumption segmenting step (S1312), the power consumption collected for each feeder or device is segmented within a given time. In other words, the power consumption is collected individually for each household appliance or feeder in order to predict the total power consumption. The power consumption collected and predicted here may be one or more of the apparent power consumption, the idling power consumption and the reactive power consumption. No-load power or reactive power, voltage, current, high frequency power samples, etc. are collected as an element of the prediction. The collected power consumption is not limited to the set elements, but may include various pieces of information related to power consumption.

Moreover, the data collected for each household appliance or feeder is segmented again at a given time. According to the embodiments described herein, the energy consumption per feeder taken per hour is shown in table 1 below.

Serial number	Feeder numbering	Date	Hour(s)	Consumption of electric power (Wh)
					1	1	2014-02-01	00	1155.423
2	1	2014-02-01	01	1000.329
					3	1	2014-02-01	02	1029.813
4	2	2014-02-01	00	903.149
					5	2	2014-02-01	01	1051.256
6	2	2014-02-01	02	1003.607
					7	3	2014-02-01	00	925.750
8	3	2014-02-01	01	1012.230
					9	3	2014-02-01	02	1002.596

TABLE 1

In one embodiment, as an example, the data collected for each feeder is segmented in time, but the data may also be segmented every 30 minutes, 15 minutes, etc. for more detailed prediction. The segment unit may be different depending on the environment, and may be different from one segment unit to another.

Also, in one embodiment, in the power consumption influencing element extracting step (S1314), at least one power consumption element that influences power consumption in the segmented power consumption is extracted.

For example, the power consumption elements that affect the change in power consumption may include indoor temperature, outdoor temperature, humidity, wind speed, sensible temperature, minute dust degree, carbon dioxide degree, minute dust, yellow dust, ozone amount, infectious disease, time, and the like. In addition to the above-described power consumption elements, other examples of the power consumption elements that may affect power consumption may include the number of persons living in a room (the number of persons that may be determined with a motion sensor or a carbon dioxide sensor), specific persons present in a room (e.g., whether or not a person with excessive power is present in a room), temperatures measured with sensors (sensors that may be installed at several points such as a room, a kitchen, etc.), position information of an indoor vehicle, and the like.

Table 2 below shows exemplary outdoor temperatures collected in an area where the electricity consumption measuring device is installed. The temperature is collected from data from the meteorological office.

Serial number	Region code	Temperature of	Observation time
				1	108	-5.7	2014-01-13 15:00:00
2	108	-6.2	2014-01-13 16:00:00
				3	108	-6.6	2014-01-13 17:00:00
4	108	-7.2	2014-01-13 18:00:00
				5	108	-7.9	2014-01-13 19:00:00
6	108	-8.1	2014-01-13 20:00:00
				7	108	-8.4	2014-01-13 21:00:00
8	108	-8.9	2014-01-13 22:00:00
				9	108	-8.6	2014-01-13 23:00:00

TABLE 2

In one embodiment, in the threshold or more power consumption element determining step (S1316), a correlation coefficient representing a correlation between the extracted power consumption elements and power consumption is calculated to determine the power consumption elements, the correlation coefficient of the power consumption elements being a predetermined threshold or more.

In other words, the correlation between the power consumption of each home appliance or feeder and the collected power consumption influence elements is analyzed. For example, a Pearson correlation coefficient or a Sperman correlation coefficient may be used to digitize the correlation between each feed line usage at a time and the outdoor temperature at the corresponding time. Specifically, from a temperature (e.g., 15 degrees celsius), it is generally felt that this temperature is convenient for calculating the correlation by the temperature, both of the correlation coefficient between the temperature lower than the corresponding temperature and the power consumption and the correlation coefficient between the temperature higher than the corresponding temperature and the power consumption, so as to set a value higher in absolute value as the correlation coefficient. This is used to complement the case where the pearson correlation coefficient is 0. An exemplary graph illustrating the application of correlation coefficients (in terms of temperature) is described in connection with fig. 14. Also, the relationship between the power consumption and the temperature of each feeder is described with reference to fig. 15.

Also, in the threshold or more power consumption element determining step (S1316), the absolute value of the correlation coefficient of each feeder line or household appliance is compared with a predetermined threshold. For example, when the predetermined threshold is 0.5, the temperature is selected as the predictive element of the

feeder lines

9 and 10 as shown in fig. 15. When the predicted elements that exert a great influence on the demand of a specific feeder or household appliance are found in advance, the step of calculating the correlation with respect to the corresponding feeder or household appliance may be omitted.

According to the foregoing exemplary embodiment, in the power consumption element extracting step (S1310), one or more power consumption elements of all feeder lines or home appliances are extracted.

Further, the relational model generation step (S1320) according to the embodiment will be described. In one embodiment, in the relational model generating step (S1320), one relational model representing the relationship between the power consumption and the power consumption element accumulated for each of the extracted power consumption elements is generated.

In other words, the usage of all the feeders and home appliances having the same power consumption factor is accumulated in a given time unit. When a particular feeder line or household appliance has unique characteristics, then not all usage is accumulated, but only usage of the respective feeder line or household appliance is considered alone. In one embodiment, if a single function is not available through any uniqueness due to the unique power consumption characteristics of the device, another function for the single device may be generated in addition to generating the usual function to enforce the relationship.

Also, the accumulated value may be a logarithm conversion value, or the original value may be used as it is. Table 3 below shows the logarithmic conversion of the total usage of the feeder which reacts sensitively to the external temperature at 10 am on a weekday and the external temperature measurement.

Serial number	Observation time	Logarithm (consumption +1)	Outside temperature
				1	2014-02-03 10:00:00	9.206443	-2.7
2	2014-02-04 10:00:00	9.131649	-8.7
				3	2014-02-05 10:00:00	9.134958	-6.4
4	2014-02-06 10:00:00	9.141014	-2.5
				5	2014-02-07 10:00:00	8.916719	1.2
6	2014-02-10 10:00:00	8.824727	0.4
				7	2014-02-11 10:00:00	7.909074	-0.3
8	2014-02-12 10:00:00	8.114223	1.7
				9	2014-02-13 10:00:00	8.756215	1.4

TABLE 3

Also, in the relational model generation step (S1320) according to the embodiment, among models that can describe the relationship between power consumption and prediction elements, a model that describes the data to the highest degree is selected, and corresponding model coefficients are extracted from the data. For example, the logarithmic conversion value of the total amount of the feeder which sensitively reacts to the external temperature may be expressed as a quadratic polynomial function of the external temperature, and the coefficient of the corresponding model may be calculated by the least square method or the like.

The various operations, acts, blocks, steps, etc. in fig. 13a and 13b may be performed in the order presented, in a different order, or concurrently. Moreover, in some embodiments, certain operations, acts, blocks, steps, etc. may be omitted, added, modified, skipped, etc., without departing from the scope of the present invention.

FIG. 14 is a graph illustrating an example of calculating correlation coefficients according to embodiments described herein. In one embodiment, this is for the case where the pearson correlation coefficient is 0 in the supplemental usage graph having the form as described in fig. 14 with respect to temperature.

FIG. 15 is a graph illustrating the relationship between power consumption and temperature for each feed line according to embodiments described herein. In one embodiment, the outside temperature of the intended building and the usage of each feeder are displayed in a graph and the Pearson correlation coefficient is recorded. Only data at 15 degrees celsius or lower is taken and only usage data during the office hours of the week (9 am to 6 pm) is considered using a characteristic (in this case, the predetermined building is an office building). Here, as the usage amount per feeder, a logarithmic conversion value (log (power consumption +1)) is used, but an original value which is not converted may be used as it is.

FIG. 16 is a graph illustrating an example of a relationship between estimated power consumption and temperature according to embodiments described herein. In one embodiment, the result of estimating the log-transformed values of the external temperature and dose according to a quadratic polynomial function is described. In addition to polynomial functions, B-Spline curves (B-Spline) and the like can be used for estimation. When one or more power consuming elements are available, a multi-dimensional surface estimation may be performed using a polynomial function or a B-spline curve function, or the like.

The margin between the calculated value and the actual observed value in each relational model is referred to as a virtual feeder or a home appliance, and then the margin is treated as a separate feeder or home appliance at the time of modeling by time series analysis so as to be patternable, which will be explained below.

All the feeder lines or the usage of the home appliances from which the power consumption elements are not extracted may be accumulated or modeled entirely using a time series analysis method such as an exponential smoothing method, an autoregressive sum moving average (ARIMA), a function analysis method, or the like. When a specific feeder line or household appliance has a special attribute with respect to time, the correspondingly extracted power consumption elements may not be accumulated and may be individually modeled.

FIG. 17 is a graph illustrating a modeling example according to embodiments described herein. In one embodiment, an example of modeling total feeder counts using a double quarter Holt-Winters method (double seastate Holt-Winters) without other predictive elements except time is described. In the method of predicting power consumption, the relational model used for prediction may be made into a (pre-stored) database, and power consumption may be predicted by receiving the relational model. Furthermore, the generated relational model can be updated according to the error value.

Fig. 18 is a flowchart illustrating a method of predicting power consumption according to other exemplary embodiments of the present invention. In one embodiment, the method includes a relational model input step (S1802), a power consumption calculation step (S1330), and a prediction information providing step (S1804).

In other words, in the relational model input step (S1802), the relational models generated by the power consumption element extraction step (S1310) and the relational model generation step (S1320) according to the foregoing embodiments are input.

Then, in the power consumption calculation step (S1330), the power consumption is calculated by the input relational model. In the predicted information providing step (S1804), additional information based on the calculated power consumption is provided. In other words, in one embodiment, in the prediction information providing step (S1804) which is a step of providing the calculated power consumption, the prediction value for each date is displayed for each pre-rendering.

Also, as additional information, the accumulated usage or the peak time of power consumption may be notified in advance. In other words, the prediction system administrator or prediction system user predicts the time to reach the predetermined cumulative usage. For example, when the cumulative usage for the relevant month belongs to cumulative 1 st order of the individual household, and a cumulative 2 nd order is expected to be entered three days later, this may be notified in advance. Also, the maximum value of the usage amount per hour on the next day and the time period for which the corresponding maximum value is generated may be notified in advance.

As additional information, an abnormal situation of the home appliance can be presumed and notified. In other words, in the present invention, the energy consumption of each feeder line or household appliance is predicted. When the actual usage of a specific feeder line or household appliance is significantly different from the predicted value, at least one corresponding household appliance or at least one household appliance connected to the corresponding feeder line may be notified that an error has occurred.

Here, "largely different" may be defined as a case where the absolute value of the difference between the predicted value Pi and the actual observed value Oi is larger than the product of the predetermined value θ multiplied by the standard deviation σ of the observed value (| Pi-Oi | > θ × σ) as described below. In this case, the predicted value and the observed value are logarithmically converted for comparison. When the predicted value and the observed value of the power consumption are compared with each other, one or more of the apparent power consumption, the no-load power consumption, and the reactive power consumption may be compared with each other.

The instantaneous values are compared in order to notify of an abnormal situation, but as shown below, when it is determined using a cumulative sum Chart (CUSUM) that Si is equal to or greater than a predetermined value, an abnormal situation may be notified.

[ equation 1]

S_i＝max(S_i+l_i，0)，

l_i＝log(P(d＝d_i|abnormal))-log(P(d＝d_i|normal))，

Here, P (d ═ 1| non-normal) and P (d ═ 1| normal) and the respective probabilities are values calculated in advance observation or a priori knowledge.

As shown in fig. 19, an apparatus that performs a method of predicting power consumption based on consumption characteristics according to an exemplary embodiment may be configured.

The various operations, acts, blocks, steps, etc. in fig. 18 may be performed in the order presented, in a different order, or simultaneously. Moreover, in some embodiments, certain operations, acts, blocks, steps, etc. may be omitted, added, modified, skipped, etc., without departing from the scope of the present invention.

Fig. 19 is a block diagram illustrating an apparatus 1900 that predicts power consumption as a function of consumption characteristics according to embodiments described herein. In one embodiment, the apparatus 1900 includes a power consumption element extraction unit 1902, a relationship model generation unit 1904, and a power consumption calculation unit 1906.

In one embodiment, in the power consumption element extraction unit 1902, the power consumption segmentation unit 1908 is configured to segment power consumption of each feeder that supplies power to each device that consumes power according to time, the influence element extraction unit 1910 is configured to extract at least one power consumption element that influences power consumption, and the power consumption element determination unit 1912 is configured to determine a power consumption element equal to or greater than a threshold value. The relational model generation unit 1904 is configured to generate a relational model representing a relationship between the accumulated power consumption of each extracted power consumption element and the power consumption element. The power consumption calculation unit 1906 is configured to calculate power consumption by the generated relational model.

Also, although not illustrated, the apparatus 1900 may be composed of a relational model input unit that receives a relational model stored in a separate database, a power consumption calculation unit that calculates power consumption through the relational model input unit, and a prediction information providing unit that provides information through the calculated power consumption.

Fig. 19 illustrates a limiting overview of an apparatus 1900 for predicting power consumption based on consumption characteristics, however, it should be understood that this is not limiting to other embodiments. The labels provided for each unit or element are for illustrative purposes only and do not limit the scope of the invention. Moreover, one or more modules may be combined or separated to perform similar or substantially similar functions without departing from the scope of the invention. Further, device 1900 may include various other components that interact with other hardware components or software components, either locally or remotely, to predict power consumption based on consumption characteristics. For example, a component may be, but is not limited to being, a program, an object, an executable, a thread of execution, a program, or a computer running in a controller and a processor.

The foregoing description of the specific embodiments reveals the general nature of the embodiments herein sufficiently that others can, by applying current knowledge, readily modify and adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, although the embodiments herein have been described with reference to preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the technical spirit and scope of the embodiments described herein.

Claims

1. An energy measurement device at a power input point for load balancing in a power management system, the energy measurement device comprising:

a power information acquisition unit configured to acquire power information of a load at a snapshot extraction frequency, wherein the snapshot extraction frequency is within a range, and the snapshot of the power information includes one of a voltage snapshot and a current snapshot of a waveform having a predetermined period as the power information; and the number of the first and second groups,

an operating state extraction unit configured to detect an operating state of at least one load device at the snapshot extraction frequency, wherein the operating state is one of a steady state and a transient state;

a data set generating unit arranged to generate a data set comprising only one snapshot or a representative snapshot of the power information upon detection of the steady state; and generating a data set comprising a plurality of snapshots of the power information upon detection of the transient.

2. The energy measuring device of claim 1, wherein said range is 10 to 900 times per second.

3. The energy measuring device of claim 1, wherein the representative snapshot is selected based on a calculation of a determination method used.

4. The energy measurement device of claim 1, further comprising a transmission unit configured to:

transmitting the only one or representative snapshot of the power information upon detection of the steady state; and the number of the first and second groups,

transmitting the plurality of snapshots of the power information upon detecting the transient.

5. The energy measurement device of claim 1, wherein the power information collection unit is configured to collect the power information, wherein the power information comprises power signals of a plurality of load devices at the power input point.

6. A server for load management in a power management system, the server comprising a controller unit arranged to:

calculating a signal correlation to reflect power information of at least one load device from a snapshot of a power signal, wherein the snapshot of the power signal is related to one of a voltage snapshot and a current snapshot of a waveform, and a remote energy measurement device measures the waveform with a predetermined period;

classifying said power information according to a component element that constructs at least one of said load devices according to said signal correlations, wherein said power information is classified as one of operating and stopped; and the number of the first and second groups,

generating a data set for at least one of the load devices based on the classified power information.

7. The server according to claim 6, wherein one of a multi-step operation and a continuous change operation is classified into one association group by one of the on-going and off-going with respect to the same load device based on the signal correlation.

8. The server according to claim 6, wherein the signal correlation comprises at least one of a voltage correlation, a current correlation, a high frequency distortion, a power signal distortion, a real power correlation, and a reactive power correlation.

9. A server according to claim 6, wherein the controller unit is arranged, when generating the data set, to:

mapping and reorganizing the sorted data sets according to the time domain; and the number of the first and second groups,

the recombined data set is labeled.

10. The server of claim 9, wherein the operational state is used to differentiate between the distribution planes of each of the load devices.

11. An energy measurement information system, the energy measurement information system comprising:

an energy measurement device configured to:

acquiring power information at a snapshot extraction frequency, wherein the snapshot extraction frequency is within a threshold, the snapshot of the power information comprises one of a voltage snapshot and a current snapshot of a waveform, and the waveform has a predetermined period as the power information;

extracting an operating state of at least one load device at the snapshot extraction frequency, wherein the operating state is one of steady-state and transient; and the number of the first and second groups,

generating and transmitting only one snapshot or a representative snapshot or a plurality of snapshots of the power information according to the running state;

a server configured to:

calculating a signal correlation to reflect power information of at least one load device from a snapshot of a power signal, wherein the snapshot of the power signal is related to one snapshot of a voltage snapshot and a current snapshot of a waveform, and an energy measurement device measures the waveform with a predetermined period;

12. The energy measurement information system of claim 11, wherein the server is configured, in generating the data set, to:

the recombined data set is labeled.

13. The energy measurement information system of claim 11, wherein the energy measurement device is configured to collect the power information, wherein the power information comprises power signals of a plurality of load devices at a power input point.

14. A method of load balancing in a power management system, the method comprising:

acquiring power information at a snapshot extraction frequency by a power information acquisition unit, wherein the snapshot extraction frequency is within a range, the snapshot of the power information includes one of a voltage snapshot and a current snapshot of a waveform, and the waveform has a predetermined period as the power information; and the number of the first and second groups,

detecting, by an operating state extraction unit, an operating state of at least one load device at the snapshot extraction frequency, wherein the operating state is one of a steady state and a transient state;

generating a data set by a data set generating unit, and generating a data set comprising only one snapshot or representative snapshot of the power information when the steady state is detected; and upon detecting the transient, generating a data set comprising a plurality of snapshots of the power information.

15. The method of claim 14, wherein the range is 10 to 900 times per second.

16. The method according to claim 14, characterized in that the representative snapshot is selected on the basis of the calculation of the measurement method used.

17. The method of claim 14, further comprising:

transmitting, by a transmission unit, the only one or a representative snapshot of the power information upon detection of the steady state; and the number of the first and second electrodes,

transmitting, by a transmitting unit, the plurality of snapshots of the power information upon detecting the transient.

18. The method of claim 14, wherein the power information collection unit is configured to collect the power information, wherein the power information comprises power signals of a plurality of load devices at a power input point.

19. A method of load management in a power management system, the method comprising:

calculating a signal correlation at a server to reflect power information of at least one load device based on a snapshot of a power signal, wherein the snapshot of the power signal is associated with one of a voltage snapshot and a current snapshot of a waveform, and a remote energy measurement device measures the waveform with a predetermined period;

classifying, at the server, the power information according to a composition unit that composes at least one of the load devices according to the signal correlation, wherein the power information is classified as one of being operated and being stopped; and the number of the first and second groups,

generating, at the server, a data set for at least one of the load devices based on the classified power information.

20. The method of claim 19, wherein one of a multi-step operation and a continuous shift operation is classified as an association group by one operation among the on-run and the off-run with respect to the same load device based on the signal correlation.

21. The method of claim 19, wherein the signal correlation comprises at least one of a voltage correlation, a current correlation, a high frequency distortion, a power signal distortion, a real power correlation, and a reactive power correlation.

22. The method of claim 19, wherein in generating the data set, the method comprises:

mapping and reorganizing, at the server, the sorted data sets according to a time domain; and the number of the first and second groups,

the reassembled data set is marked at the server.

23. The method of claim 22, wherein the operating condition is used to distinguish a distribution plane of each of the load devices.