KR20210117947A - Consumption prediction and reduction control system and consumption prediction and reduction control program for active energy management - Google Patents

Abstract

A consumption prediction and reduction control system and a consumption prediction and reduction control program for active energy management are provided. The consumption prediction and reduction control system includes a data collection part that comprises a facility data collection module for collecting information on energy facility installed in a building and an environmental data collection module for collecting information on a building environment; an energy target management part that sets an energy-related target for the building; and an energy consumption prediction part that predicts the energy consumption amount of the energy facility to reach the target set of the energy target management part based on a similar environment in the past.

Description

Consumption prediction and reduction control system and consumption prediction and reduction control program for active energy management

The present invention relates to a consumption prediction reduction control system and a consumption prediction reduction control program for active energy management, and more specifically, to predict energy consumption of energy facilities based on similar environments in the past, consumption for active energy management It relates to a predictive savings control system and a consumption predictive savings control program.

The conventional energy management system has been developed in the form of a passive system that simply monitors past data and current data.

Accordingly, the conventional energy management system collects data from various instruments and stays at the level of displaying aggregate data, so it is possible to predict demand, discover energy loss factors, or improve energy management factors such as standards for efficient facility operation. It is difficult to provide.

Accordingly, there is a need for a system capable of actively managing energy.

One technical problem to be solved by the present invention is a consumption prediction reduction control system and consumption prediction reduction control for active energy management, which collects measurement information of the entire process of production, supply, and consumption of building energy and manages it as a database to provide the program.

Another technical problem to be solved by the present invention is a consumption prediction reduction control system for active energy management, which predicts energy demand through machine learning and AI (Artificial Intelligence) based on big data accumulated and managed in the database and to provide a consumption prediction reduction control program.

Another technical problem to be solved by the present invention is a consumption prediction reduction control system and consumption prediction reduction control for active energy management, which establishes energy saving measures through machine learning and AI based on big data accumulated and managed in the database to provide the program.

The technical problem to be solved by the present invention is not limited to the above.

In order to solve the above technical problem, the present invention provides a consumption prediction reduction control system for active energy management.

According to an embodiment, the consumption prediction reduction control system for the active energy management includes a facility data collection module for collecting information on energy facilities installed in a building and an environment data collection module for collecting information on the building environment , a data collection unit, an energy target management unit that sets an energy-related target for the building, and predicts the energy consumption amount of the energy facility to reach the target set by the energy target management unit based on a similar environment in the past, energy consumption It may include a prediction unit.

According to an embodiment, the energy facility is operated according to the target set by the energy target management unit, but the actual energy used for the energy facility operation is reduced in consideration of the energy consumption predicted by the energy consumption prediction unit Further comprising an energy saving unit to control the , wherein the energy saving control unit, when the actual energy used for the energy facility operation compared to the target set by the energy target management unit deviates by more than a predetermined standard, it may notify the manager.

According to an embodiment, the energy consumption prediction unit may predict the energy consumption amount of the energy facility based on a similar environment in the past, but may further consider at least one of a day of the week and a time.

According to an embodiment, further comprising a building information collection unit that manages information on a building including a construction year, wherein the energy consumption prediction unit further considers the construction year and calculates the energy consumption amount based on the infiltration rate predictable.

According to an embodiment, further comprising an alarm processing unit for providing a warning to a manager, the energy consumption prediction unit further considers the energy analysis result of the similar building selected by the building information collection unit, the energy consumption prediction unit In the case where the predicted energy consumption in , compared to the energy analysis result of the similar building, the infiltration rate has a difference less than or equal to a predetermined standard, but the energy efficiency shows a difference greater than or equal to the predetermined standard, the alarm processing unit opens or closes the door to the manager It can provide a warning about the number of times.

In order to solve the above technical problem, the present invention provides a consumption prediction reduction control program for active energy management.

According to an embodiment, the consumption prediction reduction control program for the active energy management includes the steps of collecting information on energy facilities installed in a building, collecting information on the building environment, and determining the energy-related target of the building. and setting, and estimating the energy consumption amount of the energy facility to reach the set target based on a similar environment in the past.

According to an embodiment, the consumption prediction reduction control program for the active energy management operates the energy facility according to the set target, and considers the predicted energy consumption amount of the actual energy used for the energy facility operation. Doedoe stored in the medium to further execute the step of controlling in the direction of saving, the controlling step notifies the manager when the actual energy used for operating the energy facility in comparison to the set target deviates by more than a predetermined standard may include

According to an embodiment of the present invention, a data collection unit including a facility data collection module for collecting information on energy equipment installed in a building and an environment data collection module for collecting information on a building environment, a data collection unit, an energy-related target of the building Consumption for active energy management, comprising: an energy target management unit that sets A predictive savings control system may be provided.

Accordingly, measurement information of the entire process of production, supply, and consumption of building energy can be collected and managed as a database.

In addition, it is possible to predict energy demand through machine learning and artificial intelligence (AI) based on big data accumulated and managed by the database.

Furthermore, it is possible to establish energy saving measures through machine learning and AI based on the big data accumulated and managed in the database.

1 is a view for explaining a consumption prediction reduction control system for active energy management according to an embodiment of the present invention.
2 and 3 are diagrams for explaining a building information collection unit according to an embodiment of the present invention.
4 and 5 are diagrams for explaining a data collection unit according to an embodiment of the present invention.
6 is a view for explaining an energy target management unit according to an embodiment of the present invention.
7 is a view for explaining a data measuring unit according to an embodiment of the present invention.
8 and 9 are diagrams for explaining an energy consumption prediction unit according to an embodiment of the present invention.
10 is a diagram for explaining an energy saving control unit according to an embodiment of the present invention.
11 to 13 are diagrams for explaining a method of implementing a consumption prediction reduction control system for active energy management according to an embodiment of the present invention.

Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. However, the technical spirit of the present invention is not limited to the embodiments described herein and may be embodied in other forms. Rather, the embodiments introduced herein are provided so that the disclosed content may be thorough and complete, and the spirit of the present invention may be sufficiently conveyed to those skilled in the art.

In this specification, when a component is referred to as being on another component, it means that it may be directly formed on the other component or a third component may be interposed therebetween. In addition, in the drawings, shapes and thicknesses of regions are exaggerated for effective description of technical content.

In addition, in various embodiments of the present specification, terms such as first, second, third, etc. are used to describe various components, but these components should not be limited by these terms. These terms are only used to distinguish one component from another. Accordingly, what is referred to as a first component in one embodiment may be referred to as a second component in another embodiment. Each embodiment described and illustrated herein also includes a complementary embodiment thereof. In addition, in the present specification, 'and/or' is used to mean including at least one of the elements listed before and after.

In the specification, the singular expression includes the plural expression unless the context clearly dictates otherwise. In addition, terms such as "comprise" or "have" are intended to designate that a feature, number, step, element, or a combination thereof described in the specification is present, and one or more other features, numbers, steps, configuration It should not be construed as excluding the possibility of the presence or addition of elements or combinations thereof. Also, in the present specification, the term “connection” is used to include both indirectly connecting a plurality of components and directly connecting a plurality of components.

In addition, terms such as “…unit”, “…group”, and “module” described in the specification mean a unit that processes at least one function or operation, which may be implemented by hardware or software or a combination of hardware and software. have.

In addition, in the following description of the present invention, if it is determined that a detailed description of a related well-known function or configuration may unnecessarily obscure the gist of the present invention, the detailed description thereof will be omitted.

Hereinafter, a consumption prediction reduction control system for active energy management according to an embodiment of the present invention will be described with reference to the drawings.

1 is a view for explaining a consumption prediction reduction control system for active energy management according to an embodiment of the present invention, FIGS. 2 and 3 are views for explaining a building information collection unit according to an embodiment of the present invention, 4 and 5 are diagrams for explaining a data collection unit according to an embodiment of the present invention, FIG. 6 is a diagram for explaining an energy target management unit according to an embodiment of the present invention, and FIG. 7 is an embodiment of the present invention It is a view for explaining a data measuring unit according to the present invention, FIGS. 8 and 9 are views for explaining an energy consumption prediction unit according to an embodiment of the present invention, and FIG. 10 is a view for explaining an energy saving control unit according to an embodiment of the present invention is a drawing for

Referring to FIG. 1 , the consumption prediction reduction control system 1000 for the active energy management includes a building information collection unit 100 , a data collection unit 200 , an energy target management unit 300 , and a data measurement unit 400 . , an energy consumption prediction unit 500 , an energy saving control unit 600 , a system management unit 700 , and an alarm processing unit 800 .

Hereinafter, each configuration is described.

building information collector (100)

The building information collection unit 100 may manage information on the building year of the building. For example, the building information collection unit 100 may manage similar buildings built in a similar period for each building year as a database.

Accordingly, the energy consumption prediction unit 500 to be described later may estimate the energy consumption amount based on the infiltration rate in consideration of the construction year of the building managed by the building information collection unit 100 .

Meanwhile, referring to FIG. 2 , the building information collection unit 100 may collect basic information about a building and register it as basic data (DB_bs).

More specifically, the building information collection unit 100 collects building information (DB_bs1) including at least any one of use, floor area, shielding coefficient, and contract power, and registers it as basic data (DB_bs). can

Alternatively, the building information collection unit 100 may collect date information (DB_bs2) including at least one of a date, a holiday, a day of the week, and a season, and register it as basic data (DB_bs).

Alternatively, the building information collection unit 100 may collect a usage standard (DB_bs3) for each energy source and register it as basic data (DB_bs). Here, the energy source may be understood as a concept including an energy source used in energy facilities installed in a building, such as electricity, fuel, and heat.

Alternatively, the building information collection unit 100 may collect unit price information (DB_bs4) for each energy source and register it as basic data (DB_bs).

To this end, the building information collection unit 100 may include at least one of the building information collection module 110 and the technical data collection module 120 , again referring to FIG. 1 .

Referring to FIG. 3 , the building information collection module 110 assigns an ID (ID_bd) to each building, and location information (lc_bd), floor information (fl_bd), and zone for each building ID (ID_bd). Building basic information such as information (zn_bd) may be collected and managed as a building information database (DB_bd).

In addition, the building information collection module 110 may collect each detailed information of the basic building information and manage it as the building information database DB_bd.

More specifically, the building information collection module 110 may include an ID (ID_bd) for each building, for example, ID_bd1, ID_bd2, ID_bd3, . . . ID_bdn is assigned, and location information (lc_bd) for each building ID (ID_bd), for example, A, B, C, ... Location and floor information of building n (fl_bd) For example, the 1st to 13th floors forming the A building, the 1st to 11th floors forming the B building, the 1st to the 15th floors forming the C building, ... 1st floor to n floors forming n building, and zone information (zn_bd) For example, zone A to H forming building A, zone A to G forming building B, and zone A forming building C J-John, … Detailed information such as zone A to zone n constituting n buildings may be collected and managed as the building information database DB_bd.

Of course, the building information database DB_bd managed by the building information collection module 110 may be accumulated and stored and managed since the construction year, and thus may be utilized as big data.

The building information collection unit 100 manages the building information database DB_bd, but if there is a change in the building, the building information database DB_bd may be modified and/or deleted by the administrator. Of course.

In addition, when the building information collection unit 100 manages the building information database DB_bd, when information on a specific building floor, for example, information on the first floor of Building A is retrieved by an administrator, the building information collection module It goes without saying that 110 may provide information on the specific building floor to the manager.

Meanwhile, the technical data collection module 120 may collect and manage at least one of a building drawing and technical data.

More specifically, the technical data collection module 120 may perform drawing registration and revision management, drawing output management, and technical data registration management.

The drawing registration and revision management in the technical data collection module 120 is to be understood as a concept including managing the detailed information of the drawing to be inquired, registered, modified (or revised), and deleted by an administrator. can

In addition, the drawing output management in the technical data collection module 120 may be understood as a concept including providing a drawing file to be downloaded when an output log for a drawing is registered by an administrator.

In addition, the technical data registration management in the technical data collection module 120 may be understood as a concept including managing detailed information of technical data to be inquired, registered, modified, and deleted by an administrator. .

data collector (200)

Referring to FIG. 1 , the data collection unit 200 may include at least one of a facility data collection module 210 and an environment data collection module 220 .

The facility data collection module 210 may collect information on energy facilities installed in a building and manage it as an energy facility database.

More specifically, referring to FIG. 4 , the facility data collection module 210 may collect information on energy equipment installed in a building, for example, air conditioning equipment, air conditioning equipment, lighting equipment, and the like, and manage it as an energy equipment database. have.

To this end, the facility data collection module 210 gives an ID for each energy facility, for example, an air conditioning facility ID (ID_ar), an air conditioning facility ID (ID_ac), and a lighting facility ID (ID_lg) as shown in FIG. 4 . and the air conditioning equipment database (DB_ar), the heating and cooling equipment database (DB_ac), and the lighting equipment database (DB_lg), respectively.

Also, the facility data collection module 210 may collect detailed information for each energy facility and manage it as an energy facility database.

More specifically, referring to FIG. 4 , the facility data collection module 210 includes an ID (ID_ar) for each air conditioning facility installed in a building, for example, ID_ar1, ID_ar2, ID_ar3, ... ID_arn is assigned, and location information (lc_ar) in which the air conditioning equipment is installed for each air conditioning equipment ID (ID_ar), for example, A, B, C, ... Building n and age information (tr_ar) of the air conditioning equipment, for example, the age of the air conditioning equipment installed in building A is 1 year and 6 months, the age of the air conditioning equipment installed in building B is 1 year and 2 months, and the age of the air conditioning equipment installed in building C is 1 year and 2 months. Age 3 years 1 month, … Detailed information such as the age of n years and n months of air conditioning equipment installed in building n may be collected and managed as the air conditioning equipment database DB_ar.

In addition, the facility data collection module 210 includes an ID (ID_ac) for each heating and cooling facility installed in a building, for example, ID_ac1, ID_ac2, ID_ac3, ... ID_acn is given, and location information (lc_ac) where the heating and cooling equipment is installed for each heating and cooling equipment ID (ID_ac), for example, A, B, C, ... Building n, and age information (tr_ac) of the heating and cooling equipment, for example, the age of the heating and cooling equipment installed in Building A is 2 years and 2 months, the age of the heating and cooling equipment installed in Building B is 2 years and 6 months, the age of the heating and cooling equipment installed in Building C Age 1 year and 3 months, … Detailed information such as the age of n years and n months of heating and cooling facilities installed in building n may be collected and managed with the heating and cooling facility database (DB_ac).

In addition, the facility data collection module 210 includes an ID (ID_lg) for each lighting facility installed in a building, for example, ID_lg1, ID_lg2, ID_lg3, ... ID_lgn is given, and location information (lc_lg) where the lighting equipment is installed for each lighting equipment ID (ID_lg), for example, A, B, C, ... Building n and the age information (tr_lg) of the lighting equipment, for example, the age of the lighting equipment installed in Building A is 1 year and 8 months, the age of the lighting equipment installed in Building B is 1 year and 9 months, and the age of the lighting equipment installed in Building C is 1 year and 9 months. Age 1 year and 8 months, … Detailed information such as the age of n years and n months of lighting equipment installed in building n may be collected and managed by the lighting equipment database (DB_lg).

Meanwhile, the energy equipment is not limited to the above-described examples, ie, air conditioning equipment, air conditioning equipment, and lighting equipment, and may be understood as a concept including all energy equipment installed in a building.

Of course, the energy facility database managed by the facility data collection module 210 may be accumulated, stored and managed since the energy facility is installed in a building, and thus may be utilized as big data.

Meanwhile, the environment data collection module 220 may collect information on the building environment and manage it as a building environment database. Here, the building environment may mean an external environment of the building. For example, the building environment may be understood as a concept including outdoor temperature, outdoor humidity, outdoor solar illuminance, outdoor wind direction, and the like.

More specifically, referring to FIG. 5 , the environment data collection module 220 assigns a building environment ID (ID_ev) to each building, and location information (lc_ev) and temperature information (tp_ev) for each building environment ID (ID_ev) , and information on the building environment such as humidity information (hm_ev) may be collected and managed as a building environment database (DB_ev).

Also, the environment data collection module 220 may collect each detailed information of the building environment information and manage it as the building environment database DB_ev.

More specifically, the environment data collection module 220 may generate an ID (ID_ev) for each building environment, for example, ID_ev1, ID_ev2, ID_ev3, ... ID_evn is given, and location information (lc_ev) for each building environment ID (ID_ev), for example, A, B, C, ... Location and temperature information of building n (tp_ev) For example, outside temperature of building A (tp_ev1), outside temperature of building B (tp_ev2), outside temperature of building C (tp_ev3), ... External temperature (tp_evn) and humidity information (hm_ev) of building n, for example, external humidity of building A (hm_ev1), external humidity of building B (hm_ev2), external humidity of building C (hm_ev3), ... Detailed information such as the external humidity (hm_evn) of the n building may be collected and managed by the building environment database (DB_ev).

To this end, the environmental data collection module 220 may be interlocked with an environmental information measuring device, for example, a temperature sensor, a humidity sensor, etc. provided at a specific control point outside the building.

Of course, the building environment database DB_ev managed by the environment data collection module 220 may be accumulated and stored and managed since the construction year, and thus may be utilized as big data.

Accordingly, the energy consumption prediction unit 500 to be described later uses the building environment database DB_ev managed by the environmental data collection module 220 as big data to estimate the energy consumption of the energy facility based on the similar environment in the past. can be predicted with

Meanwhile, it goes without saying that the building environment database DB_ev managed by the environment data collection module 220 may be inquired, registered, modified, and deleted by an administrator.

On the other hand, when the building environment database DB_ev managed by the environmental data collection module 220 is inquired by an administrator, the measurement history during the inquiry period may be provided through at least one of a detailed list and a graph. .

Energy target management unit (300)

The energy target management unit 300 may set an energy-related target for the building.

More specifically, referring to FIG. 6 , the energy target management unit 300 sets an energy target used to operate energy equipment installed in a building, for example, air conditioning equipment, air conditioning equipment, lighting equipment, etc. It can be managed as a star target energy database.

To this end, the energy target management unit 300 gives each energy facility an ID, for example, an air conditioning facility ID (ID_ar), an air conditioning facility ID (ID_ac), and a lighting facility ID (ID_lg) as shown in FIG. 6 , , respectively, can be managed by the air conditioning equipment target energy database (DB_gl_ar), the heating and cooling equipment target energy database (DB_gl_ac), and the lighting equipment target energy database (DB_gl_lg).

Also, the energy target management unit 300 may collect detailed information for each energy facility and manage it as a target energy database for each energy facility.

More specifically, referring to FIG. 6 , the energy target management unit 300 includes an ID (ID_ar) for each air conditioning facility installed in a building, for example, ID_ar1, ID_ar2, ID_ar3, ... ID_arn is assigned, and location information (lc_ar) in which the air conditioning equipment is installed for each air conditioning equipment ID (ID_ar), for example, A, B, C, ... Building n, and energy target information (gl_ar) used to operate the air conditioning equipment, for example, the energy target (gl_ar1) used to operate the air conditioning equipment installed in Building A, and used to operate the air conditioning equipment installed in Building B Energy target (gl_ar2), energy target used to operate air conditioning equipment installed in C building (gl_ar3), … Detailed information such as an energy target (gl_arn) used to operate the air conditioning equipment installed in building n may be collected and managed as the air conditioning equipment target energy database (DB_gl_ar).

In addition, the energy target management unit 300, the ID (ID_ac) for each heating and cooling equipment installed in the building, for example, ID_ac1, ID_ac2, ID_ac3, ... ID_acn is given, and location information (lc_ac) where the heating and cooling equipment is installed for each heating and cooling equipment ID (ID_ac), for example, A, B, C, ... Building n, and energy target information (gl_ac) used to operate the heating and cooling facilities, for example, the energy target (gl_ac1) used to operate the heating and cooling facilities installed in Building A, used to operate the heating and cooling facilities installed in Building B Energy target (gl_ac2), energy target used to operate heating and cooling facilities installed in Building C (gl_ac3), … Detailed information such as an energy target (gl_acn) used to operate the heating and cooling facility installed in building n may be collected and managed as the heating/cooling facility target energy database (DB_gl_ac).

In addition, the energy target management unit 300, ID (ID_lg) for each lighting equipment installed in the building, for example, ID_lg1, ID_lg2, ID_lg3, ... ID_lgn is given, and location information (lc_lg) where the lighting equipment is installed for each lighting equipment ID (ID_lg), for example, A, B, C, ... Building n and energy target information (gl_lg) used to operate the lighting equipment For example, the energy target (gl_lg1) used to operate the lighting equipment installed in Building A, and used to operate the lighting equipment installed in Building B Energy target (gl_lg2), energy target used to operate lighting equipment installed in Building C (gl_lg3), … Detailed information such as an energy target (gl_lgn) used to operate lighting equipment installed in building n may be collected and managed as the lighting equipment target energy database (DB_gl_lg).

Of course, the target energy database for each energy facility managed by the energy target management unit 300 may be accumulated, stored and managed since the energy facility is installed in a building, and thus may be utilized as big data.

Accordingly, in the energy consumption prediction unit 500 to be described later, the target energy database for each energy facility managed by the energy target management unit 300 is used as big data to reach the target energy database for each energy facility. Energy consumption can be predicted based on similar circumstances in the past.

Meanwhile, the energy target management unit 300 may set the energy-related target of the building in consideration of at least one of a similar environment in the past and a building year of the building.

For example, when the energy target management unit 300 sets an energy target for a specific building, it is used in a similar environment in the past for the specific building, for example, for the operation of energy facilities of the specific building during the summer of last year. Considering the amount of energy used, it is possible to set an energy-related target for the specific building this summer.

In this case, it is assumed that the energy equipment installed in the specific building will be used from the last year to the current year.

Or, for another example, the energy target management unit 300, in setting the energy target of the specific building, considering the amount of energy used for energy facility operation in other buildings of the same building year as the building year of the specific building. , it is possible to set an energy-related target for the specific building.

In this case, it is assumed that the construction years of the specific building and the other building are similar, but the energy facility installation time of the other building precedes the energy facility installation time of the specific building. In this case, since the construction years of the specific building and the other building are similar, it can be considered that the infiltration rates of the specific building and the other building are similar.

Meanwhile, it goes without saying that the setting of the energy-related target in the energy target management unit 300 may be based on the energy consumption amount predicted by the energy consumption prediction unit 500 to be described later.

Meanwhile, the energy target management unit 300 may further manage energy cost target management and energy usage target achievement status.

More specifically, the energy cost target management in the energy target management unit 300 is managed so that the cost target of each energy source for the year for each building can be inquired, registered, modified, and deleted by the manager. It can be understood as a concept that includes

In addition, the energy use target achievement status in the energy target management unit 300 may be understood as a concept including providing the manager with a list of energy use, target amount, and achievement rate in a designated year for each building.

data measuring part (400)

The data measurement unit 400 may measure information on a specific control point of the building where energy is used by the energy facility. Herein, the specific control point of a building may be understood as a concept including a unit to which energy is supplied by the energy facility, for example, a building unit, a floor unit, and a house unit.

In addition, the information on the specific control point of the building may be understood as a concept including the indoor temperature, indoor humidity, indoor solar illuminance, indoor wind direction, and the like for each unit.

More specifically, referring to FIG. 7 , the data measuring unit 400 assigns a control point ID (ID_cp) to each specific control point of the building, location information (lc_cp), and temperature information for each control point ID (ID_cp) (tp_cp) and humidity information (hm_cp) may collect information on a specific control point of the building where energy is used, and manage it as a specific control point database (DB_cp) of the building.

More specifically, the data measuring unit 400 may include a control point ID (ID_cp) for each specific control point of a building, for example, ID_cp1, ID_cp2, ID_cp3, . ID_cpn is assigned, and location information (lc_cp) for each control point ID (ID_cp), for example, A, B, C, ... Location and temperature information of building n (tp_cp) For example, the indoor temperature of building A (tp_cp1), the indoor temperature of building B (tp_cp2), the indoor temperature of building C (tp_cp3), … Indoor temperature (tp_cpn) and humidity information (hm_cp) of building n, for example, indoor humidity of building A (hm_cp1), indoor humidity of building B (hm_cp2), indoor humidity of building C (hm_cp3), ... Detailed information such as indoor humidity (hm_cpn) of building n may be collected and managed as a specific control point database (DB_cp) of the building.

To this end, the data measuring unit 400 may be interlocked with an indoor information measuring device, for example, a temperature sensor, a humidity sensor, etc. provided at a specific control point inside the building. Alternatively, the data measuring unit 400 may be linked with a measuring instrument connected to a specific control point inside the building, for example, electricity, gas, water, or flow metering instruments. In this case, the data measuring unit 400 may further manage the measurement information.

The specific control point database (DB_cp) of the building managed by the data measurement unit 400 may be accumulated and stored and managed since the construction year and/or the energy facility installation year, and thus, utilized as big data Of course it could be.

Accordingly, in the energy consumption prediction unit 500 to be described later, the specific control point database (DB_cp) of the building managed by the data measurement unit 400 and the building managed by the environmental data collection module 220 described above The energy consumption can be predicted by using the environmental database (DB_ev) as big data.

Meanwhile, it goes without saying that the specific control point database DB_cp of the building managed by the data measurement unit 400 may be inquired, registered, modified, and deleted by an administrator. Alternatively, the measuring instrument information managed by the data measuring unit 400, ie, measuring instrument installation information and product detailed information, may be inquired, registered, modified, and deleted by an administrator.

On the other hand, when the specific control point database (DB_cp) of the building managed by the data measurement unit 400 is inquired by an administrator, the measurement history for the inquiry period is provided through at least one of a detailed list and a graph. can

energy consumption predictor (500)

The energy consumption prediction unit 500 may predict the energy consumption amount of the energy facility to reach the set target of the energy target management unit 300 based on a similar environment in the past.

To this end, as described above, the energy consumption prediction unit 500 manages the building environment database (DB_ev, see FIG. 5 ) managed by the environmental data collection module 220 and the data measurement unit 400 . It is possible to utilize the specific control point database (DB_cp, see FIG. 7) of the old building as big data.

In addition, the energy consumption prediction unit 500 may perform machine learning based on the big data or use artificial intelligence (AI).

Accordingly, as described above, the energy consumption prediction unit 500 may predict the energy consumption amount based on a similar environment in the past in consideration of the information on the building environment.

On the other hand, the energy consumption prediction unit 500, in the energy consumption prediction, by further considering the construction year of the building managed by the building information collection unit 100 as described above, based on the infiltration rate The energy consumption can be predicted. Accordingly, the energy consumption prediction unit 500 according to an embodiment of the present invention may accurately predict the amount of energy consumption in which the degree of deterioration of the building is reflected.

In addition, the energy consumption prediction unit 500, in the energy consumption prediction, predicts the energy consumption amount of the energy facility based on a similar environment in the past, further considering at least any one of the day and time, the energy consumption can be predicted. This is in consideration of the fact that energy consumption may be affected by day of the week and/or time of day.

To this end, the energy consumption prediction unit 500, as shown in FIG. 1, selects at least one of an energy clustering module 510, an energy conversion module 520, and an energy consumption prediction result providing module 530. may include

Referring to FIG. 8 , the energy clustering module 510 may cluster (DB_pr1), ie, group, energy consumption at a specific control point of a building among the big data by energy source, use, and zone.

Accordingly, energy usage may be clustered in the big data and classified by the group.

Meanwhile, in the energy conversion module 520 , the energy usage in the big data clustered in the clustering module 510 may be converted (DB_pr2).

More specifically, in the energy conversion module 520 , the energy consumption may be converted into energy amount TOE (Ton of Oil Equivalent), and/or converted into CO2 emission, and/or converted into energy unit usage.

Accordingly, the unit of energy usage in the big data may have uniformity.

On the other hand, the energy consumption prediction result providing module 530 is clustered in the energy clustering module 510 and performs machine learning or AI based on big data on the energy usage converted by the energy conversion module 520 . can be used to predict (DB_pr3) energy consumption for each clustering.

In this case, the energy consumption prediction result providing module 530 may provide the predicted energy consumption amount on the screen according to the manager's search condition.

To this end, referring to FIG. 9 , the energy consumption prediction result providing module 530 may provide a search window to the manager. Here, the search window may be provided in the form of a search bar (br) as shown in FIG. 9 or may be provided as a word input window.

When the administrator sets a specific search period (year (yr), month (mn), day (dy)) using the search bar (br), the energy consumption prediction result providing module 530 is A forecast of energy consumption for a period can be provided to the manager on the screen. For example, as shown in FIG. 9, at the current time point A (year 2021 (yr) January (mn) 6 days (dy) 15:00 (tm)), the manager mn) 6 days (dy) When searching for a prediction for the energy consumption to be used in the air conditioning facility on the 1st floor of Building A today, the energy consumption prediction result providing module 530 is, The energy consumption used for floor cooling, that is, the accumulated usage (us), may be provided from the data measurement unit 400 .

On the other hand, the energy consumption prediction result providing module 530 utilizes the big data and performs machine learning for the accumulated usage (us) up to the current time point A (15:00 (tm)) of the past by using AI. It can be prepared for similar environments.

Accordingly, the energy consumption prediction result providing module 530 is, based on the similar environment in the past, time B after the current time A (15:00) (for example, 30 minutes after the current time A) , time C (eg, 1 hour after current time A), time D (eg, 2 hours after current time A), time E (eg 1 hour and 30 minutes after current time A) It is possible to predict the amount of energy consumption in

Meanwhile, in the energy target management unit 300 , the energy-related target is set as described above, and the energy consumption prediction result providing module 530 provides the energy to reach the set target of the energy target management unit 300 . consumption can be predicted.

Hereinafter, the energy-related target set in the energy target management unit 300 is the energy used for the operation of the air conditioning facility so that the indoor temperature of a specific control point of the building, for example, the first floor of Building A is 25 degrees (target temperature). to assume that

For example, referring to FIG. 9 , for example, when the energy target gl used for the operation of the air conditioner is set to 70 kWh so that the indoor temperature of the first floor of Building A becomes 25 degrees (target temperature), the energy The consumption prediction result providing module 530 may predict that the energy target (gl) 70 kWh will be reached at time D, 2 hours after the current time point A (15:00, room temperature 30 degrees), and provide it as a screen. .

In this case, the energy consumption prediction result providing module 530 may further consider the energy consumption prediction result of, for example, a similar building built at a time similar to that of Building A selected by the building information collection unit 100 , for example, Building B. have.

In the energy consumption predicted by the energy consumption prediction result providing module 530, the infiltration rate of Building A compared to the energy analysis result of Building B, which is the similar building, may have a difference of less than or equal to a predetermined standard. Here, the predetermined reference may mean a reference value set by an administrator.

Meanwhile, in the energy consumption predicted by the energy consumption prediction result providing module 530 , the energy efficiency of the building A compared to the energy analysis result of the building B, which is the similar building, may have a difference of more than a predetermined standard. Here, the predetermined reference means a reference value set by an administrator, of course.

Meanwhile, the energy consumption prediction result providing module 530 may predict the energy consumption amount of the energy facility based on a similar environment in the past, but may further consider at least one of a day of the week and a time. This is in consideration of the fact that energy consumption may be affected by day of the week and/or time of day. For example, when preparing for Wednesday on weekdays and Sunday on weekends, if more time is spent at home on Sundays and Sundays on weekends than on Wednesdays on weekdays, energy consumption at home, for example, electricity consumption, may increase. Alternatively, when preparing for Wednesday on weekdays and Sunday on weekends, if there is more time at home on Sundays and Sundays on weekends than on Wednesdays on weekdays, energy consumption at home, for example, heating use, may decrease. In this case, it is assumed that there are many people staying at home, and the use of heating is reduced by the warmth of people.

On the other hand, the energy consumption prediction result providing module 530, in providing the energy consumption as a screen, for a specific time, for example, the shortest energy consumption of 1 hour or more to 3 hours or less, for example, predicts the electrical energy demand This can be provided as a line graph and a list.

Energy saving control unit (600)

The energy saving control unit 600 operates the energy facility according to the set target of the energy target management unit 300 , but taking into account the energy consumption predicted by the energy consumption prediction unit 500 , the energy facility operation It can be controlled in such a way that the actual energy used for the operation is reduced.

For example, referring to FIG. 9 , after the energy target (gl) 70 kWh set so that the indoor temperature of the first floor of Building A is 25 degrees is reached, the indoor temperature of the first floor of Building A is 27 degrees, and the energy target ( gl), even if the actual energy is used, the indoor temperature of the first floor of Building A may still be 27 degrees.

In this case, there may be causes such as aging of buildings, energy facilities, that is, aging of cooling facilities.

In this case, even if the energy used to operate the energy facility, that is, the air conditioning facility, exceeds the energy target gl, the indoor temperature does not drop any more, so the This can lead to wasted energy.

Accordingly, the energy saving unit 600 operates the cooling facility so that the indoor temperature of the specific control point, that is, the first floor of Building A, becomes the limit of 27 degrees by interlocking with the alarm notification unit 800 to be described later and notifying the manager. The energy target used in the project may be reset by the manager.

In this case, the energy consumption prediction unit 500 may, of course, utilize the above-described big data and perform machine learning or use AI, even in resetting the energy target. In other words, the energy consumption prediction unit 500 may predict, for example, the amount of energy consumption of the air conditioning equipment for the indoor temperature of the first floor of Building A to be the limit of 27 degrees.

Accordingly, according to an embodiment of the present invention, the energy saving unit 600 operates the energy facility according to the set target of the energy target management unit 300, but reaches a limit value, and the energy target management unit ( 300), when the actual energy used for the operation of the energy facility deviates by more than a predetermined standard, in consideration of the energy consumption predicted by the energy consumption prediction unit 500, the energy used for the operation of the energy facility is used It can be controlled in a direction in which real energy is saved. Accordingly, the state of the energy facility, for example, old age, may be taken into consideration, and actual energy used for the operation of the energy facility may be reduced.

Alternatively, the energy saving unit 600 may automatically control the set energy target to be automatically adjusted according to a predetermined standard when the actual energy used for the operation of the energy facility deviates by more than a predetermined standard. Here, the predetermined reference may mean a reference value set by an administrator.

On the other hand, on the other hand, it can be assumed that the old energy facility is replaced with a new energy facility.

In this case, the energy saving control unit 600, the environmental information of the specific control point before reaching the set energy target, for example, when it is determined that the indoor temperature has reached the target temperature, the alarm processing unit 800 to be described later This can be communicated to the administrator in conjunction with

Accordingly, the actual energy used for the operation of the energy facility may be reduced.

Alternatively, when it is determined that the indoor temperature first reaches the target temperature before the set energy target is reached, the energy saving unit 600 may automatically control it to automatically adjust it according to a predetermined standard. Here, the predetermined reference may mean a reference value set by an administrator.

On the other hand, unlike the above, it can be assumed that the above-described limit value is reached even though it is not a problem of the aging of buildings and energy facilities.

In this case, in the energy saving unit 600, it can be analyzed that there is a problem of lowering energy efficiency at the specific control point, for example, building A. For example, among the causes of the decrease in the energy efficiency of air conditioning equipment in Building A, the number of openings and closings in Building A may be higher than in a similar building.

Accordingly, in the warning processing unit 800 to be described later linked with the energy saving unit 600, a warning about the number of times of door opening and closing, for example, checking the number of times of door opening and closing, and checking whether the door is open It can give you a warning to do it.

As described above, in order to perform notification control and/or automatic control to the manager, the energy saving control unit 600, as shown in FIG. 1, an energy peak setting module 610 and a control setting module ( 620) may include at least one of them.

Accordingly, in the energy saving control unit 600 , control reference data may be registered (DB_ct) by at least one of the energy peak setting module 610 and the control setting module 620 .

Referring to FIG. 10 , the control reference data registration (DB_ct) includes a power peak alarm range setting (DB_ct1) by the energy peak setting module 610 and an alarm range for each energy facility by the control setting module 620 . It may include at least one of setting (DB_ct2), lighting control step registration by illuminance (DB_ct3), facility automatic control standard registration (DB_ct4), and automatic control procedure registration by alarm (DB_ct5).

System management unit (700)

The system manager 700 may perform at least one of reference information management, operator management, authority management, and log view management.

Standard information management in the system management unit 700, common code information, BEMS (Building Energy Management System, building energy management system) code information, energy source information, standard environment control point information, standard control point information, power time zone classification , may include at least one of power unit price information, drawing classification information, and technical data classification information management.

The common code information management in the system management unit 700 may include managing the common code major and intermediate categories of the EM system (Energy Management System) to be registered and modified.

The BEMS code information management in the system management unit 700 may include managing the energy-related common code major and intermediate categories of the EM system to be registered and modified.

Energy source information management in the system management unit 700 may include providing a list of all registered heat source information to an administrator, and managing to be inquired, registered, modified, and deleted.

The management of standard environment control point information in the system management unit 700 may include providing an administrator with all registered standard environment control point information as a list, and managing it so that it can be inquired, registered, modified, and deleted. .

The standard control point information management in the system management unit 700 may include providing a list of all registered standard control point information to an administrator, and managing it so that it can be inquired, registered, modified, and deleted.

The division of power time zones in the system management unit 700 may include managing to provide a list of all registered power time zones to the administrator, and to be inquired, registered, modified, and deleted.

The power unit price information management in the system manager 700 may include providing a list of all registered power unit price information to the manager, and managing to be inquired, registered, modified, and deleted.

The management of the drawing classification information in the system management unit 700 may include managing the large and medium classifications of drawings to be registered, corrected, and deleted.

Management of technical data classification information in the system management unit 700 may include managing technical data sub-classification information to be inquired, registered, modified, and deleted.

The worker management in the system management unit 700 may include at least one of user management, appointment and dismissal management, court education management, and partner company management.

User management in the system management unit 700 may include managing user information to be inquired, registered, modified, and deleted.

Appointment and dismissal management in the system management unit 700 may include managing appointment and dismissal information so that it can be inquired, registered, corrected, and deleted.

The legal education management in the system management unit 700 may include managing the information of the legal education graduates to be inquired, registered, modified, and deleted.

The partner company management in the system management unit 700 may include managing partner company information to be inquired, registered, and modified.

The authority management in the system manager 700 may include at least one of menu information management and menu authority information management.

The management of menu information in the system management unit 700 may include managing menu information so that it can be inquired, registered, and modified for each TOP level, 1st level, and 2nd level.

The management of the menu right information in the system manager 700 may include managing the use right of the menu for each user classification.

The log view management in the system manager 700 may include at least one of usage log view management and exception occurrence log view management.

The usage log view management in the system manager 700 may include managing the program access log view for each user and per day.

The management of the exception occurrence log view in the system manager 700 may include managing the view of the occurrence log for an unexpected error in the system.

Meanwhile, the system manager 700 may further perform login management, logout management, and password change management.

Log-in management in the system manager 700 may include entering the BEMS when an administrator logs in.

Logout management in the system manager 700 may include terminating the use of BEMS when the administrator logs out.

Password change management in the system management unit 700 may include changing the password by an administrator.

Meanwhile, the system manager 700 may further provide a user manual view.

Viewing the user's manual provided by the system management unit 700 may include showing the BEMS user manual and providing a download.

Meanwhile, in the system management unit 700 , at least one of air tool and tool management and facility information management may be further managed.

The air tool and tool management in the system management unit 700 may include at least one of tool information management, calibration history management, repair history management, and tool status management.

The tool information management in the system management unit 700 may include managing the detailed information of the tool so that it can be inquired, registered, modified, and deleted.

The calibration history management in the system management unit 700 may include managing the calibration information of the specific tool to be calibrated so that it can be inquired, registered, corrected, and deleted.

The repair history management in the system management unit 700 may include managing the repair history of a specific tool to be inquired, registered, modified, and deleted.

The tool status management in the system manager 700 may include managing to provide detailed information of tools that satisfy the inquiry condition in a list.

The facility information management in the system manager 700 may include at least one of facility classification management, facility information management, cooling system interworking information management, and power system interworking information management.

The facility classification management in the system management unit 700 may include managing all registered equipment sub-categories to be provided as a list, and to be registered, modified, and deleted.

The facility information management in the system management unit 700 may include managing all registered facilities as a list and being registered, modified, and deleted.

The cooling system interworking information management in the system management unit 700 may include providing mapping information for each accessory of the cooling system package as a list, and managing so that it can be registered, modified, and deleted.

The power system interworking information management in the system manager 700 may include managing the interworking mapping information for each item of the power system for each panel and managing it so that it can be inquired, registered, modified, and deleted.

Alert processing unit (800)

The alert processing unit 800 may provide a warning to the manager.

More specifically, the alarm processing unit 800 may provide a warning to the manager when the information on the specific control point of the building measured by the data measurement unit 400 deviates by more than a predetermined standard. will be. Here, the predetermined reference may mean a reference value set by an administrator.

For example, when the energy consumption used in the air conditioning facility in Building A measured by the data measurement unit 400 deviates by more than the predetermined standard, the alarm processing unit 800 may provide a warning to the manager. there will be

Accordingly, the manager checks the specific control point that has deviated by more than the predetermined standard, and checks whether it is an energy facility failure or an unexpected error occurring in the consumption prediction reduction control system 1000 for active energy management. can

On the other hand, the alarm processing unit 800, as described above, in the energy saving unit 600, but operates the energy facility according to the set target of the energy target management unit 300, but at the limit value described above. When the actual energy used for the energy facility operation compared to the target set by the energy target management unit 300 deviates by more than a predetermined standard, the manager may be notified.

Accordingly, the energy target used for the operation of the energy facility may be reset by the manager.

Alternatively, as described above, the alarm processing unit 800, in the energy saving control unit 600, before reaching the set energy target, environmental information of the specific control point, for example, the indoor temperature reaches the target temperature. If it is determined that this has been done, it may be notified to the manager.

On the other hand, the alarm processing unit 800, as described above, among the analysis results predicted by the energy consumption prediction unit 500, the infiltration rate between similar buildings has a difference of less than or equal to a predetermined standard, but the energy efficiency is A warning can be provided to the manager if it shows a difference beyond a set criterion.

For example, as described above, in the case where Building A and Building B are similar buildings, the infiltration rate of Building A compared to the energy analysis result of Building B has a difference of less than or equal to a predetermined standard, but If the energy efficiency of Building A compared to the energy analysis result has a difference of more than a predetermined standard, a warning can be provided to the manager about the number of times the door is opened and closed.

As described above, a consumption prediction reduction control system for active energy management according to an embodiment of the present invention has been described.

Hereinafter, a method of implementing a consumption prediction reduction control system for active energy management according to an embodiment of the present invention will be described.

11 to 13 are diagrams for explaining a method of implementing a consumption prediction reduction control system for active energy management according to an embodiment of the present invention.

Referring to FIG. 11 , the method of implementing the consumption prediction reduction control system for the active energy management includes the steps of collecting information on energy facilities installed in a building (S110), collecting information on the building environment (S120), It may include at least one of the step of setting the energy-related target of the building (S130), and the step of predicting the energy consumption of the energy facility to reach the set target based on the similar environment in the past (S140).

Hereinafter, each step is described.

Step S110

In step S110, the facility data collection module 210 may collect information on energy facilities installed in a building and manage it as an energy facility database.

As for the facility data collection module 210 , as previously described, a duplicate description in this step will be omitted.

Step S120

In step S120 , the environment data collection module 220 may collect information on the building environment and manage it as a building environment database. Here, the building environment may mean an external environment of the building, as described above. For example, the building environment may be understood as a concept including outdoor temperature, outdoor humidity, outdoor solar illuminance, outdoor wind direction, and the like.

As for the environmental data collection module 220, as previously described, a duplicate description in this step will be omitted.

Step S130

In step S130 , the energy target management unit 300 may set an energy-related target for the building.

As for the energy target management unit 300 , as described above, a duplicate description in this step will be omitted.

Meanwhile, although the above-described steps S110 to S130 have been sequentially described, they are not limited to the above-described order, and the order may be changed or may be performed simultaneously. For example, steps S110 and S120 may be performed simultaneously.

On the other hand, although not shown in the drawings, the building information collection unit 100, of course, can manage information on the year of construction of the building. The information management for the building year in the building information collection unit 100, the above-described steps S110 and S120 may be performed earlier, or may be performed simultaneously with at least one of the steps S110 and S120.

Step S140

In step S140 , the energy consumption prediction unit 500 may predict the energy consumption amount of the energy facility to reach the target set by the energy target management unit 300 based on a similar environment in the past.

To this end, as described above, the energy consumption prediction unit 500 manages the building environment database (DB_ev, see FIG. 5 ) managed by the environmental data collection module 220 and the data measurement unit 400 . It is possible to utilize the specific control point database (DB_cp, see FIG. 7) of the old building as big data.

In addition, the energy consumption prediction unit 500 may perform machine learning based on the big data or use AI.

Accordingly, as described above, the energy consumption prediction unit 500 may predict the energy consumption amount based on a similar environment in the past in consideration of the information on the building environment.

On the other hand, the energy consumption prediction unit 500, in the energy consumption prediction, by further considering the construction year of the building managed by the building information collection unit 100 as described above, based on the infiltration rate The energy consumption can be predicted. Accordingly, the energy consumption prediction unit 500 according to an embodiment of the present invention may accurately predict the amount of energy consumption in which the degree of deterioration of the building is reflected.

In addition, the energy consumption prediction unit 500, in the energy consumption prediction, predicts the energy consumption amount of the energy facility based on a similar environment in the past, further considering at least any one of the day and time, the energy consumption can be predicted. This is in consideration of the fact that energy consumption may be affected by day of the week and/or time of day.

As for the energy consumption prediction unit 500 , as previously described, a duplicate description in this step will be omitted.

On the other hand, although not shown in the drawings, after the above-described step S140, step S150 may be further performed.

Step S150

In step S150, the energy saving control unit 600 operates the energy facility according to the set target of the energy target management unit 300, but taking into account the energy consumption predicted by the energy consumption prediction unit 500, It is possible to control the actual energy used for the operation of the energy facility to be reduced.

Meanwhile, in this step, the alert processing unit 800 operates the energy facility according to the set target of the energy target management unit 300 in the energy saving unit 600 as described above, but as described above. When the limit value is reached and the actual energy used for the energy facility operation is deviated by more than a predetermined standard compared to the target set by the energy target management unit 300 , the manager may be notified.

As for the energy saving control unit 600 and the alarm processing unit 800 , as described above, overlapping descriptions in this step will be omitted.

Meanwhile, referring to FIG. 12 , the energy saving control unit 600 may control the actual energy saving direction for each facility. To this end, the automatic control standard loading step for each facility (S210), the indoor environment control point data collection step (S220), the control point data collection step for each facility (S230), and the facility control execution step for each automatic control standard (S240) ), at least one of the steps may be performed.

Hereinafter, each step is described.

Step S210 to Step S240

In step S210, an automatic control standard for each facility may be loaded into the energy saving control unit 600 .

In this case, the automatic control criteria for each facility loaded in the energy saving control unit 600 may be set by an administrator.

In step S220, the data measuring unit 400 may collect indoor environment control point data.

The description of the data measuring unit 400 will be referred to the above-described embodiment.

In step S230, the facility data collection module 210 may collect control point data for each facility.

The description of the facility data collection module 210 will refer to the above-described embodiment.

When the indoor environment control point data is collected through step S220 and the control point data for each facility is collected through step S230, the energy saving control unit 600 determines whether an operation rate adjustment is necessary depending on whether the energy facility is in operation It can be determined whether or not operation is necessary.

Accordingly, in step S240 , the energy saving control unit 600 may perform facility control for each automatic control standard according to the automatic control standard loading for each facility set by the manager in the previous step S210 .

Referring to FIG. 13 , steps S310 to S340 will be described assuming a lighting facility as an example of steps S210 to S240.

Referring to FIG. 13 , an automatic lighting control standard for each illuminance loading (Loading) step (S310), a illuminance measurement value collection step for each zone (S320), a lighting off alarm sending step (S330), and an illuminance level At least one of the star lighting control performing steps (S340) may be performed.

Hereinafter, each step is described.

Step S310 to Step S340

In step S310 , the energy saving control unit 600 may be loaded with an automatic lighting control range for each illuminance.

In this case, the automatic lighting control range for each illuminance loaded in the energy saving control unit 600 may be set by an administrator.

In step S320 , the data measuring unit 400 may collect an illuminance measurement value for each zone.

The description of the data measuring unit 400 will be referred to the above-described embodiment.

On the other hand, the energy saving control unit 600 determines whether the measured illuminance value measured by the data measuring unit 400 corresponds to an off level of the automatic lighting control range for each illuminance set by the manager in the previous step S310, and the off If the level corresponds to the automatic off with the lighting, in step S330, it is possible to send a lighting off alarm to the manager through the alert processing unit 800 linked.

Alternatively, when the energy saving control unit 600 determines that the illuminance measurement value measured by the data measuring unit 400 does not correspond to the off-level among the illuminance automatic control ranges set by the administrator in the previous step S310, step In S340 , lighting control may be performed for each illuminance level.

Meanwhile, according to an embodiment, the method of implementing the consumption prediction reduction control system for the active energy management may be implemented through a consumption prediction reduction control program for the active energy management.

More specifically, the consumption prediction reduction control program for the active energy management includes the steps described above, that is, the method for implementing the consumption prediction reduction control system for the active energy management includes collecting information on energy facilities installed in a building. (S110), collecting information on the building environment (S120), setting an energy-related target of the building (S130), and predicting the energy consumption of energy facilities to reach the set target based on the similar environment in the past At least one of the step (S140), and the step (S150) of operating the energy facility according to the set target, but controlling the actual energy used for the energy facility operation in a direction in which the actual energy used for the operation of the energy facility is reduced in consideration of the predicted energy consumption It may be stored on a medium for execution.

Here, the medium may be understood as a concept including a predetermined terminal such as a desktop, a laptop computer, a web pad, or a smart phone.

Alternatively, the consumption prediction reduction control program for the active energy management may be stored in the server to execute the steps described above, of course.

As mentioned above, although the present invention has been described in detail using preferred embodiments, the scope of the present invention is not limited to specific embodiments and should be construed according to the appended claims. In addition, those skilled in the art should understand that many modifications and variations are possible without departing from the scope of the present invention.

100: building information collection unit
110: building information collection module
120: technical data collection module
200: data collection unit
210: facility data collection module
220: environmental data collection module
300: Energy Target Management Department
400: data measurement unit
500: energy consumption prediction unit
510: energy clustering module
520: energy conversion module
530: Energy consumption prediction result providing module
600: energy saving control unit
610: energy peak setting module
620: control setting module
700: system management unit
800: alarm processing unit
1000: Consumption Predictive Savings Control System for Active Energy Management

Claims (7)

A data collection unit comprising: a facility data collection module for collecting information on energy equipment installed in a building; and an environment data collection module for collecting information on a building environment;
an energy target management unit that sets an energy-related target for the building; and
Consumption prediction and reduction control system for active energy management, including; an energy consumption prediction unit for predicting the amount of energy consumption of the energy facility to reach the target set by the energy target management unit based on a similar environment in the past.
According to claim 1,
In accordance with the target set by the energy target management unit, the energy facility is operated, but the energy consumption predicted by the energy consumption prediction unit is taken into consideration, and the actual energy used for the energy facility operation is controlled in a direction to reduce energy saving, further including wealth,
The energy saving control unit,
Consumption prediction reduction control system for active energy management, which notifies a manager when the actual energy used for the operation of the energy facility is more than a predetermined standard compared to the target set by the energy target management unit.
According to claim 1,
The energy consumption prediction unit,
Predict the energy consumption of the energy facility based on the similar environment in the past,
Consumption prediction reduction control system for active energy management, which further considers at least any one of day and time.
According to claim 1,
Further comprising a building information collection unit, which manages information about the building including the year of construction,
The energy consumption prediction unit, further considering the construction year, predicts the energy consumption based on the infiltration rate, a consumption prediction reduction control system for active energy management.
5. The method of claim 4,
Further comprising an alert handling unit, which provides an alert to the manager,
The energy consumption prediction unit further considers an energy analysis result of the similar building selected by the building information collection unit, wherein the energy consumption predicted by the energy consumption prediction unit is a predetermined infiltration rate compared to the energy analysis result of the similar building. If there is a difference below the standard, but energy efficiency shows a difference greater than the predetermined standard,
The alarm processing unit, a consumption prediction reduction control system for active energy management, which provides a warning about the number of times the door is opened and closed to the manager.
collecting information about energy equipment installed in the building;
collecting information on the building environment;
setting an energy-related target for the building; and
Predicting the energy consumption of the energy facility to reach the set target based on a similar environment in the past; stored in the medium to execute, a consumption prediction reduction control program for active energy management.
7. The method of claim 6,
In order to further execute the step of operating the energy facility according to the set target, and controlling the actual energy used for the operation of the energy facility in a direction in which the actual energy used for the operation of the energy facility is reduced in consideration of the predicted energy consumption, it is stored in the medium,
The controlling step is
Consumption prediction reduction control program for active energy management, comprising notifying a manager when the actual energy used for the operation of the energy facility is more than a predetermined standard compared to the set target.

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