KR20220132710A - Method for reading remotely meters in building based on AI and home network system - Google Patents

Abstract

The present invention provides a remote meter reading method including the steps of: (a) building, by an energy analysis AI server (100), an energy analysis AI model by using household information and meter reading values of a corresponding household; (b) calculating an energy consumption reference value for an input household when household information of a household for which remote meter reading is performed is input to the energy analysis AI model; (c) automatically performing remote meter reading at a meter reader (430) of the household by a household energy management module (420) provided for each household, thereby checking a meter reading value; (d) transmitting the checked meter reading value to a remote meter reading server (210) through a home network server (200); and (e) providing, by a meter reading data analysis module (222) of the remote meter reading server (210), a notification to a terminal of the corresponding household or a manager terminal when the meter reading value checked in step (c) is greater than a preset upper limit ratio or less than a preset lower limit ratio based on the energy consumption reference value calculated in step (b). The present invention is to perform remote meter reading more economically.

Description

AI and home network-based remote meter reading method {Method for reading remotely meters in building based on AI and home network system}

The present invention relates to a remote meter reading method in which an energy analysis AI model is built based on big data obtained from a plurality of complexes, and by using this, accurate remote meter reading can be made and an abnormality of the meter reader can be immediately confirmed.

Recently, as a remote meter reading technology has been developed, the number of households in which a meter reader does not visit each household, remotely checks the meter reading value, calculates the amount of use, and charges each household is increasing. For this, a system interface unit (SIU), a separate repeater for remote meter reading, and piping wiring are required. Such a system can be confirmed in Korean Patent Registration No. 10-0565826, which is a registered patent of the present applicant.

Since remote meter reading is performed by a separate technology rather than the naked eye of the meter reading staff, errors often occur during meter reading depending on the completeness of the technology. In case of incorrect billing due to incorrect reading, it will cause great inconvenience. In addition, there is a problem in that it takes a lot of time and money to build a system for remote meter reading such as SIU, separate repeater, and piping wiring.

On the other hand, despite technological advances, with respect to meter reading, only checking the meter reading value according to the traditional method. For example, if the meter reader is out of order, it is difficult to know it in a short period of time unless the meter reader has a self-examination function. In most cases, when a specific household has a gas meter reading of 0 for several months, it is only confirmed by a separate investigation by a gas supplier or manager who wondered about it.

Korean Patent No. 10-0565826 Korean Patent Publication No. 2016-0040401 Korean Patent Publication No. 2009-0010564 Korean Patent No. 10-1812589 Korean Patent Publication No. 2020-0071639 Korean Patent Registration No. 10-1570144

The present invention has been devised to solve the above problems.

Specifically, it is intended to perform remote meter reading more economically by utilizing the home network system already built in an apartment house without a separate SIU, repeater, or pipe wiring.

In particular, when there is an abnormality in the meter reader, it is immediately confirmed and a method is proposed to reduce the number of false meter reading cases.

If a large amount of driving data can be obtained from multiple complexes, we will build an AI model based on this and propose a new concept of remote meter reading method that can immediately identify suspicious cases without self-examination of the meter reader. do.

An embodiment of the present invention for solving the above problems, (a) the energy analysis AI server 100, building an energy analysis AI model using generation information and the meter reading value of the corresponding generation; - Here, the household information includes only regional characteristics including households, characteristics of households including households, and household characteristics- (b) When household information of a household in which remote meter reading is performed is input to the energy analysis AI model, input calculating a reference value of energy usage for the old household; (c) automatically performing remote meter reading in the household meter reader 430 by the household energy management module 420 provided for each household and checking the meter reading value; (d) transmitting the checked meter reading value to the remote meter reading server 210 through the home network server 200; and (e) the meter reading data analysis module 222 of the remote meter reading server 210, based on the energy usage reference value calculated in step (b), sets the meter reading value confirmed in step (c) to a preset value. Provided is a remote meter reading method, which includes providing a notification to a terminal of a corresponding household or a manager's terminal when it is excessive by more than the upper limit ratio or too little by less than a preset lower limit ratio.

In addition, the step (c) includes the step of (c1) transmitting the energy data identified from the sensor 440 of the household equipment provided for each household to the energy data management server 290 through the home network server 200. Further comprising, -here, the energy data of the household equipment includes the usage of the household equipment- The step (e) includes: (e1) the meter reading expected value using the usage amount of the household equipment identified in the transmitted energy data and providing a notification to the manager terminal when the expected meter reading value and the meter reading value confirmed in step (c) are excessive by more than a preset upper limit ratio or under a preset lower limit ratio or less it is preferable

In addition, in step (e), (e2) when the remote meter reading server 210 receives an abnormal signal from the meter reader 430, the remote meter reading server 210 notifies the manager terminal of the meter reader abnormality. It is preferable to further include the step of providing.

In addition, the meter reader 430 performs self-examination according to a preset cycle, and in step (e), (e3) the remote meter reading server 210 sends an abnormality confirmation signal from the meter reader 430 to the self-examination result. When receiving, the remote meter reading server 210 may further include providing a meter reader abnormality notification to the manager terminal.

In addition, the meter reading value in step (a) is the meter reading value confirmed in the trial operation energy data and operation energy data, and the complex area characteristic in step (a) includes the location and weather information of the complex, and the characteristics preferably includes the orientation of the dong within the complex, and the household characteristics include the location and amount of sunlight in the dong.

Preferably, the household information further includes occupant characteristics, and the occupant characteristics include the number of household members, the occupation of each household member, the gender of each household member, and the age of each household member.

In addition, the meter reader 430 includes a water meter reader 434 and a power meter reader 435 located outside the household, and the meter reader 430 further includes a gas meter reader 431, or a hot water meter reader 432 ) and it is preferable to further include a heating meter reader (433).

In addition, the step (d) includes: (d1) transmitting the meter reading value confirmed in step (c) to the household hub 400; (d2) transmitting the meter reading value transmitted to the generation hub 400 to the switching hub 300 provided for each unit; (d3) transmitting the meter reading value transmitted to the switching hub 300 to the home network server 200; and (d4) transmitting the meter reading value transmitted to the home network server 200 to the remote meter reading server 210 .

According to the present invention, by utilizing AI, the unique characteristics of each household (regional location, weather, orientation, location in the neighborhood, sunlight, bay, etc.) The usage reference value is calculated with high accuracy. Based on this, it is possible to identify a generation in which an excessive or under-measured reading value has been confirmed, thereby preventing an erroneous reading problem due to an abnormality in the meter reading or wiring.

In addition, by further considering the household equipment usage identified in each household, it is possible to check whether the meter reader is abnormal more quickly and with high accuracy.

Generation information used to build AI models is very diverse and vast. Only regional characteristics, dynamic characteristics, and generation characteristics are all considered, and in one embodiment, even occupant characteristics are considered to build an AI model with high accuracy. The AI model constructed in this way can be used as useful information not only for determining whether the meter reading is abnormal according to the present invention, but also for material analysis of buildings in the future, construction planning, and the like.

It is economical because it does not require additional equipment installation because it utilizes the home network system already built in the apartment house without a separate SIU, repeater, or pipe wiring.

1 and 2 show a system in which a method according to the invention is carried out.
3 schematically shows an energy analysis AI server 100 in which the method according to the invention is performed.
4 schematically shows a remote meter reading server 210 on which a method according to the present invention is performed.
5 is a conceptual diagram for explaining an AI model construction method in the method according to the present invention.
6 is a flowchart illustrating a method according to the present invention.

Hereinafter, "complex" means a set of one or more buildings. When the building is an apartment, a building set composed of a plurality of apartment buildings is a complex, and when the building is an office, a single-dong office or a multi-dong office set is a complex. “Only” includes one or more “dongs”.

Hereinafter, "household" means a plurality of unit spaces constituting the building of the complex. If the building is an apartment, each unit dwelling is a household, and if the building is an office, each unit office is a household. Many generations come together to form a building.

Hereinafter, the term “household terminal” refers to a terminal of a resident household, and may be a concept including a resident's personal terminal (smartphone, etc.) as well as a wall pad located in each household.

Hereinafter, "manager" means a subject who manages the corresponding building. The manager may be a construction company, or may be a company specializing in management.

1. Description of the system for performing the remote meter reading method

A system in which the method according to the present invention is performed will be described with reference to FIGS. 1 to 4 .

The present invention builds and utilizes an energy analysis AI model, as shown in FIG. 1 , a plurality of home network servers 200 in which an energy analysis AI server 100 for building an energy analysis AI model is provided for each complex. It is electrically connected to and receives and learns a lot of data (big data).

With further reference to FIG. 3 , the energy analysis AI server 100 builds an "energy analysis AI model" for predicting the reference value of energy used in the corresponding household when entering household information, and uses this to build "energy usage reference value for each household" It performs the function of predicting ".

Generation information and actual meter reading values are input to build an energy analysis AI model. The actual meter reading value is included in the trial operation energy data and operation energy data respectively input from the trial operation energy data input unit 121 and the operation energy data input unit 122 .

The test run energy data input unit 121 inputs energy data that can be checked during test run as information for building an AI model. Here, "test run" means that after the building is completed and before moving in, the construction company mainly operates it as a test to inspect the building, and "drive" mainly refers to the operation for checking the performance of various household facilities (eg, Turning on the lights, turning on the faucet, and operating the air conditioning equipment). In a newly built building, bake out, which increases the indoor temperature in order to remove harmful gases or substances from the building before moving in, is a concept included in this test run. For example, the boiler is continuously operated for bakeout during test operation. At this time, the meter reader 430 will continue to operate, and the meter reading value confirmed in this process is included in the test operation energy data, and this data is used for building an energy analysis AI model. It is used as learning information.

The driving energy data input unit 122 checks the driving energy data and inputs it as information for building an AI model. The meter reading value of the meter reader 430 is checked while the actual tenant moves in and lives, and this information is included in the driving energy data, and this data is also used as learning information for building an energy analysis AI model.

Meanwhile, generation information for building the energy analysis AI model is only input through the regional characteristic input unit 123 , the dynamic characteristic input unit 124 , and the generation characteristic input unit 125 . In an embodiment of the present invention, occupant characteristics may be further learned through the occupant characteristics input unit 126 . Each piece of information is detailed in the AI model building method below.

When the energy analysis AI model is built in this way, information of a household for which an actual meter reading value is to be predicted is input through the generation-by-generation energy usage reference value calculation unit 127, and the energy usage reference value can be calculated.

On the other hand, the AI used herein may be any technology. Various technologies such as machine learning, deep learning, CNN, RNN, ANN, and fuzzy theory methods can be applied.

Again, referring to FIG. 2 , the home network server 200 is provided in each complex, and performs remote meter reading through the switching hub 300 provided in each building and the household hub 400 provided in each household. It communicates with the household energy management module 420 .

In order to perform remote meter reading, the household energy management module 420 is connected to each meter reader 430 . The meter reader 430 includes a water meter reader 434 and an electric power meter reader 435 located outside the household, and further includes a gas meter reader 431 for individual heating households, and a hot water meter reader for district heating or central heating households. 432 and a heating meter reader 433 are further included. Here, although expressed as a gas meter reader 431, a generation using renewable energy may include a meter reader that reads other meterable energy sources.

The meter reading value may be transmitted to the home network server 200 through the household hub 400 and the switching hub 300 , and may also be transmitted to the remote meter reading server 210 . In addition, since it is directly connected to the wall pad 410 , the resident can check the meter reading value of his/her household in real time through the wall pad 410 .

A method for the generation energy management module 420 to perform remote meter reading is not limited, and any known technique may be applied. For example, it may be electrically connected to the meter reader 430 to receive an electrical signal, or a text reading technique may be applied after capturing an image.

Since the household energy management module 420 is connected to the home network server 200, a separate SIU for remote meter reading, a repeater for remote meter reading, and piping wiring can be omitted, which is economical. In addition, it is possible to prevent mixing of piping and wiring between households in an apartment house, and confusion due to incorrect construction.

On the other hand, the household hub 400 is connected to the sensors 440 provided in various household facilities (eg, boilers, lighting, outlets, system air conditioners, etc.) that consume energy within the household, so that the actual water and electricity of the household facilities. , gas (or hot water and heating) usage can be checked in real time. The checked usage is included in the energy data and transmitted to the energy data management server 290 . The usage amount confirmed during the test operation will be included in the above-described test operation energy data, and the usage amount confirmed during operation will be included in the operation energy data described above. These provide information necessary to check whether the meter reader 430 is abnormal, and a specific method will be described later.

Referring to FIG. 4 , the remote meter reading server 210 includes a system management module 211 , a real-time meter reading module 212 , an inquiry analysis module 220 , and a usage settlement module 213 .

The system management module 211 performs functions such as operator registration/modification, operator change, data management, SMS setup, server environment setup, meter reader setup and confirmation, and network management of the remote meter reading server 210 .

The real-time meter reading module 212 receives and manages the meter reading value from the household energy management module 420 located in each household.

The inquiry analysis module 220 includes a meter reading data inquiry module 221 that provides a function of inquiring the meter reading data, a meter reading data analysis module 222 that analyzes the meter reading data to check whether the meter reader or wiring is abnormal, and an operation history for each operator. and an operation history inquiry module 223 for inquiring.

In addition, through the energy analysis AI server 100, for checking the energy usage reference value for each generation, it further includes a generation-specific energy usage reference value confirmation module 224.

In addition, the meter reader abnormal signal analysis module 225 receives an abnormal signal transmitted when the meter reader 430 checks itself for abnormality, and receives an abnormality confirmation signal during periodic self-examination of the meter reader 430 to check whether the meter reader is abnormal. ) and a meter reader self-examination module 226 .

In addition, electricity usage history usage comparison module 227, heating and hot water usage history usage comparison module 228 (district heating, central heating) and a boiler usage history usage comparison module 229 (individual heating).

On the other hand, the remote meter reading server 210 further includes a usage settlement module 213 for calculating the usage billed to each household based on the meter reading value.

2. Description of remote meter reading method

A remote meter reading method according to the present invention will be described with reference to FIGS. 5 and 6 .

First, the energy analysis AI server 100 builds an energy analysis AI model using generation information and the meter reading value of the corresponding generation (S610). It will be described in detail with reference to FIG. 5 .

The information learned for building the energy analysis AI model is information about each household (right of FIG. 5) and the meter reading value of the corresponding household (left of FIG. 5) for a plurality of households of a plurality of houses in a plurality of complexes. The meter reading value includes a water meter reading value and an electric power meter reading value, and in the case of an individual heating household, a gas meter reading value, and a district heating household or central heating household further includes a hot water meter reading value and a heating meter reading value. An individual AI model is built for each meter reading value.

The learned generation information includes only regional characteristics including the generation, the characteristics of the same area including the generation, and the generation characteristics. In one embodiment, it may further include an occupant characteristic.

The "regional characteristics of the complex" are defined as the regional characteristics of the complex. It may include the location of the complex and weather information at the location. The weather information may include an average temperature, average rainfall/snowfall, average wind speed, and the like. Buildings in relatively cold regions such as the metropolitan area and Gangwon-do and buildings in relatively hot regions such as Gyeongsang-do, Jeolla-do and Jeju-do are constructed with the same materials and consume different energy even if the occupants live in the same pattern. Since the energy consumption of buildings in local and non-local buildings is different, this information is used.

"Sign characteristics" are defined only as characteristics of my consent. Dynamic properties may include orientation. For example, it may be divided into south-facing, east-facing, west-facing, southeast-facing, southwest-facing, or the like, or an azimuth with respect to the north (N) may be used. It is used as learning information because the energy consumption of the building is different depending on the orientation.

"Generation characteristics" is defined as the architectural characteristics of individual generations. Household characteristics may be more diverse, and may include the location (top floor, bottom floor, sidewall layer, center floor, etc.) of the household in the dong, amount of sunlight, floor area, number of bays, insulation used, and the like. These information are also variables that affect the energy consumption of the corresponding generation.

This generation information is utilized, and the actual meter reading value of the corresponding generation is learned as learning information. The actual meter reading value includes both the meter reading value confirmed in the test operation energy data and the meter reading value confirmed in the operation energy data.

For example, in the case of 101-dong 102 (individual heating) in Seoul Complex A, regional characteristics such as location and weather information of complex A as household information, dong characteristics such as the orientation (orientation) of 101-dong, and No. 102 are the lowest floor and the side wall It does not form an area of 100m2, has a three-bay structure, and is equipped with insulation K. The household characteristics are input, and the water, electricity, and gas meter readings measured during the test operation of 101-102, 101-dong, Seoul Complex, and the number that are confirmed after the occupants actually move in , electric power, and gas meter reading values are input and utilized as learning information.

The contractor can verify this information in several complexes. That is, there are dozens of buildings in each complex, and there are dozens of households in each building, so it is possible to secure a large number of test operation energy data and operation energy data for each household.

Using the built energy analysis AI model, it is possible to estimate the energy use of a household by specifying the generation. This is referred to as the reference value of energy consumption by household. The reference value of energy consumption for each household is a concept that collectively refers to water, electric power, and gas (or hot water and heating).

For example, if household information (complex regional characteristics, dong characteristics, household characteristics) for 1501, 105-dong, Gyeonggi-do is input to the energy analysis AI model, the energy consumption standard value for each household is output. This indicates how common the meter reading values of water, electric power, and gas are in this generation of environments.

Meanwhile, according to an embodiment of the present invention, occupant characteristics may be further included and learned as generation information. The occupant characteristics include the number of household members, the occupation of each household member, the gender of each household member, and the age of each household member. In other words, it is possible to further increase the accuracy of the energy analysis AI model by using the living patterns of residents.

The number of household members, the occupation of each household member, the gender of each household member, and the age of each household member affect the living pattern of the resident, and the living pattern of the resident influences the energy used by each household.

For example, if the number of household members is large, energy consumption increases. If a household member's job is to work at night (the characteristics of each job are stored in a separate database), the nighttime energy consumption will be high. If there are children in the household, the indoor temperature in winter will be set higher and the indoor temperature lower in summer, so energy consumption will increase.

For another example, in a new generation with four members, the occupation (gender, age) of each household member is the first office worker (male, 45), the second housewife (female, 40), and the third person student (male, 10), 4th student (female, 8), this occupant information is included in household information, and as a result of household information analysis, the residence time of the household in the morning on weekdays will be relatively small, and the residence time of the household on weekends/holidays It is inferred that it will be relatively large, and reflecting this, the energy consumption that will increase proportionally as the residence time increases is estimated.

As described above, since various occupant characteristics will affect energy consumption, by inputting household information including occupant characteristics into the built energy analysis AI model, the energy consumption of the household is estimated with higher accuracy.

Again, returning to FIG. 6, in the state in which the energy usage reference value of the household for which remote meter reading is to be performed using the already built energy analysis AI model is confirmed, the The remote meter reading is automatically performed by the meter reader 430 (S621). The remote meter reading method may use any known technique.

When the remote meter reading is performed and the meter reading value is confirmed, it is transmitted to the remote meter reading server 210 . Specifically, the automatically checked meter reading value is transmitted to the household hub 400 , transmitted to the switching hub 300 provided for each building, and then transmitted to the home network server 200 , and then to the remote meter reading server 210 . transmitted (see FIG. 2).

Meanwhile, the meter reading value transmitted to the home network server 200 may be transmitted to the energy analysis AI server 100 together with generation information, and the transmitted information is also used as learning information for building an energy analysis AI model.

Simultaneously with the remote meter reading, in real time, energy data is automatically checked from the sensor 440 of the household equipment provided for each household, and this is transmitted to the energy data management server 290 through the home network server 200 (S622). . As described above, the energy data of household equipment includes usage of household equipment. The usage amount is utilized in step S661.

As described above, while the remote meter reading and usage of household equipment are checked in real time, when the meter reader 430 itself determines that there is an abnormality (S631), an abnormal signal is generated. The generated abnormal signal is transmitted to the meter reader abnormal signal analysis module 225 of the remote meter reading server 210 through the meter reading data analysis module 222 and the home network server 200 . If the meter reader abnormal signal analysis module 225 receives the abnormal signal, a problem has occurred in the meter reader 430, and thus, a meter reader abnormality notification is provided to the manager terminal (S632). Upon receiving the notification, the manager immediately visits the household to repair the meter reader 430 .

The meter reader 430 may periodically (eg, once a month) perform self-examination. When it is determined that there is an abnormality as a result of the self-examination of the meter reader 430 ( S641 ), similarly, an abnormality confirmation signal is generated. The generated abnormality confirmation signal is transmitted to the meter reader self-examination module 226 of the remote meter reading server 210 through the meter reading data analysis module 222 and the home network server 200 . If the meter reader self-examination module 226 receives an abnormality confirmation signal, since a problem has occurred in the meter reader 430, a meter reader error notification is provided to the manager terminal (S632), and the manager immediately visits the household to repair the meter reader 430 will do

In addition, the meter reading data analysis module 222 of the remote meter reading server 210, based on the energy usage reference value confirmed in the energy analysis AI model, is excessive by a preset upper limit ratio (eg, 50%) or more or a preset lower limit It can be determined that the ratio is too small (eg, 50%) or less (S651). In this case, there may be an abnormality in the meter reader 430, or it may be that a special life pattern is created in the household (for example, an unusually large number of visitors visit in the month) and excessive or insufficient energy is used. There may be no abnormality in the meter reader 430 . Accordingly, in this case, a notification is provided not only to the manager's terminal but also to the terminal of the corresponding household (S652).

If the resident of the corresponding household confirms this, he/she checks his/her energy usage and, if it is determined that the meter reading value is excessive, requests the management company to inspect the meter reader 430, thereby preventing unreasonably overcharging the management fee.

If the manager confirms this, in particular, when the meter reading value is too small, the inspection of the meter reader 430 is directly performed, thereby preventing a claim from being omitted.

In addition, the electricity usage history usage comparison module 227, the heating and hot water usage history usage comparison module 228 for district heating/central heating households, and the boiler usage history usage comparison module 229 for individual heating households, S622 The meter reading expected value is calculated using the usage of household equipment of energy data that is checked in real time in step S621, and the calculated meter reading expected value and the meter reading value confirmed in step S621 are excessive by a preset upper limit ratio (eg, 30%) or more. Or, it is checked whether it is too small by a preset lower limit ratio (eg, 30%) or less (S661). If it is excessive, the meter reading value is higher than that of the household equipment used, and it is possible to predict an abnormality in the meter reading device or an abnormality in wiring construction. The same is true if it is too small. An abnormality in the meter reader or wiring construction is predicted. In this case, a notification is provided to the manager's terminal (S882).

In this way, rather than simply performing remote meter reading, it is possible to check in real time whether the meter reader is abnormal by using the meter reading value predicted using the AI model, and check the actual usage of the household equipment to determine whether the meter reader or wiring is abnormal. can be checked In addition, by providing a notification to the manager terminal or both the manager terminal and the household terminal depending on the occurrence of an abnormality, the problem of erroneous reading is fundamentally prevented. From the tenant's point of view, it is possible to prevent being charged for unused energy, and from the manager's point of view, it is possible to prevent omission of billing. In addition, as data is accumulated in this way, the AI model is continuously trained, so the accuracy will continue to increase.

In the above, the present specification has been described with reference to the embodiments shown in the drawings so that those skilled in the art can easily understand and reproduce the present invention, but these are merely exemplary, and those skilled in the art may make various modifications and equivalent other modifications from the embodiments of the present invention. It will be appreciated that embodiments are possible. Therefore, the protection scope of the present invention should be defined by the claims.

100: Energy Analysis AI Server
110: Energy analysis AI model building unit
121: commissioning energy data input unit
122: operating energy data input unit
123: only regional characteristic input unit
124: dynamic characteristic input unit
125: generation characteristic input unit
126: occupant characteristic input unit
127: Energy consumption reference value calculation unit for each household
200: home network server
210: remote meter reading server
211: system management module
212: real-time meter reading module
220: query analysis module
221: meter reading data inquiry module
222: meter reading data analysis module
223: operation history inquiry module
224: module for checking the reference value of energy usage by household
225: meter reader abnormal signal analysis module
226: meter reader self-examination module
227: electricity usage history usage comparison module
228: Heating hot water usage history usage comparison module
229: Boiler usage history usage comparison module
213: usage settlement module
290: energy data management server
300: switching hub
400: Generation Hub
410: wall pad
420: generation energy management module
430: meter reader
431: gas meter reader
432: hot water meter reader
433: heating meter reader
434: water meter reader
435: power meter reader
440: sensor

Claims (8)

(a) constructing, by the energy analysis AI server 100, an energy analysis AI model using household information and the meter reading value of the corresponding generation; -Here, the household information includes only regional characteristics including households, dong characteristics including households, and household characteristics-
(b) calculating an energy usage reference value for the input household when household information of a household for which remote meter reading is performed is input to the energy analysis AI model;
(c) automatically performing remote meter reading in the household meter reader 430 by the household energy management module 420 provided for each household and checking the meter reading value;
(d) transmitting the checked meter reading value to the remote meter reading server 210 through the home network server 200; and
(e) the meter reading data analysis module 222 of the remote meter reading server 210, based on the energy usage reference value calculated in the step (b), sets the meter reading value confirmed in step (c) to a preset upper limit In the case of excessive over the ratio or too little below the preset lower limit ratio, comprising the step of providing a notification to the terminal of the household or the manager's terminal,
Remote meter reading method.
The method of claim 1,
Step (c) is,
(c1) further comprising the step of transmitting energy data identified from the sensor 440 of the household equipment provided for each household to the energy data management server 290 through the home network server 200, - Here, the household equipment Energy data of household includes usage of facilities-
Step (e) is,
(e1) Calculating an expected meter reading value using the usage of household equipment identified in the transmitted energy data, and comparing the meter reading expected value with the meter reading value confirmed in step (c) is excessive by more than a preset upper limit ratio or when it is too low below a preset lower limit ratio, comprising the step of providing a notification to the manager terminal,
Remote meter reading method.
The method of claim 1,
Step (e) is,
(e2) when the remote meter reading server 210 receives an abnormal signal from the meter reader 430, the remote meter reading server 210 further comprising the step of providing a meter reader abnormality notification to the manager terminal;
Remote meter reading method.
The method of claim 1,
The meter reader 430 performs self-examination according to a preset cycle,
Step (e) is,
(e3) when the remote meter reading server 210 receives an abnormality confirmation signal as a result of the self-examination from the meter reader 430, the remote meter reading server 210 further includes the step of providing a meter reader abnormality notification to the manager terminal doing,
Remote meter reading method.
The method of claim 1,
The meter reading value in step (a) is the meter reading value confirmed in the test operation energy data and the operation energy data,
The complex regional characteristics in step (a) include the location and weather information of the complex, the dong characteristics include the orientation of the dong within the complex, and the household characteristics include the location and the amount of sunlight in the dong,
Remote meter reading method.
6. The method of claim 5,
The household information further includes occupant characteristics,
The occupant characteristics include the number of household members, the occupation of each household member, the gender of each household member, and the age of each household member;
Remote meter reading method.
The method of claim 1,
The meter reader 430 includes a water meter reader 434 and a power meter reader 435 located outside the household,
The meter reader 430 further includes a gas meter reader 431, or further includes a hot water meter reader 432 and a heating meter reader 433,
Remote meter reading method.
The method of claim 1,
Step (d) is,
(d1) transmitting the meter reading value confirmed in step (c) to the household hub 400;
(d2) transmitting the meter reading value transmitted to the generation hub 400 to the switching hub 300 provided for each unit;
(d3) transmitting the meter reading value transmitted to the switching hub 300 to the home network server 200; and
(d4) transmitting the meter reading value transmitted to the home network server 200 to the remote meter reading server 210;
Remote meter reading method.

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