KR101917729B1 - Average power consumption control system - Google Patents

Abstract

More particularly, the present invention relates to a system and method for controlling power consumption averaging for saving electricity charges in apartment houses, and more particularly, And more particularly, to a system and method for reducing management cost by smart use of electric energy by suppressing peak electric power.
The power consumption averaging control system for saving the electricity cost of the apartment house according to the embodiment of the present invention includes a plurality of electric power consumption averaging control systems for each house constituting the apartment house, A large-scale data DB that stores previous electric energy usage pattern information (big data), and a management system for a common housing facility And predicts the use of electric energy based on the predicted model of the use of electric energy stored in the big data DB and the big data, and receives the electric energy usage amount from the consumer electric appliance and the electricity use setting value from the user terminal A housing management book that controls the electricity use of electric appliances by creating electricity energy use schedules .

Description

[0001] The present invention relates to an average power consumption control system,

More particularly, the present invention relates to a system and method for controlling power consumption averaging for saving electricity charges in apartment houses, and more particularly, And more particularly, to a system and method for reducing management cost by smart use of electric energy by suppressing peak electric power.

As the industry develops, the use of lighting, air-conditioning, and various household appliances in homes, schools, and government offices is increasing, and accordingly more electric energy is being used.

Furthermore, due to the recent excessive power consumption, power shortage phenomenon frequently occurs and systematic management of power consumption is required.

Meanwhile, according to KEPCO, the peak power of each year in Korea is steadily rising by about 5% every year due to the supply of cold, radiators, large household appliances, unreasonable use of electricity, temperature drop and economic growth.

As the peak power increases, the power reserve ratio drops, and it is necessary to increase the power production facilities to secure a stable power reserve. However, it is difficult to solve the problems due to the increase of the secured location, enormous investment funding and enforcement of environmental regulations.

Therefore, peak power management is indispensable in order to reduce power supply stability and power dissipation.

Electricity charges are contracted annually, and the standard at the time of contract is calculated by adding the charge of electricity used per month as the basic charge for the next year as the charge of the peak electric power among the annual electric power used for the previous year, which is checked every 15 minutes.

Therefore, in order to reduce the base rate, the maximum demand power (peak) value of July to September should be managed.

Electricity charges such as schools and government offices are also charged as the sum of the basic charge plus the usage fee.

In order to reduce the basic charge, it installs and operates a peak power controller by PC control for the system air conditioners installed on the site, thereby saving utility charges and saving energy.

As stated above, the peak electricity tariff will be applied when the load capacity of the electric power is over 100KW. As mentioned above, the basic electricity charge is based on the peak power for 15 minutes during the three months from July to September in summer Because it charges for 12 months, once high peak power is determined, it will have to pay a huge base rate over a year.

The reasons for deciding the annual maximum demand for power including the base rate from July to September are as follows. First, it is to maintain the fairness of the cost burden (principle of cost burden charging).

In other words, since the maximum load during the year is mainly due to the electricity usage patterns during the period from July to September, and the construction of the power plant is also based on the peak load in July and September, the basic charges, This is because it is based on the principle of equality that it is linked to the annual base rate based on the maximum demand power generated in a month.

The second reason is to reduce the cost of constructing power plants through demand management and to curb electricity tariff hikes.

If the base rate is calculated based on the annual maximum demand for electricity including the current month and July to September, it will be a strong factor for consumers to save electricity in July and September as much as possible.

However, in the summer, air-conditioning loads account for more than 50% of all schools. Therefore, air conditioning is not operated to reduce the basic charge, or managers must operate loads such as air conditioners.

Registration No. 10-1359040 (Publication date February 21, 2014)

SUMMARY OF THE INVENTION The present invention is conceived to solve the above-mentioned problems, and it is an object of the present invention to reduce the maximum power amount of the apartment house to manage peak electric power, reduce the basic charge of electric charges through peak power management, The present invention is directed to a system and method for averaging power consumption for saving electricity bills in a residential house.

According to an aspect of the present invention, there is provided a power consumption averaging control system for saving electricity bills in a cooperative house, the system comprising: A consumer electronic device having a sensor function for recognizing usage amount;

Internet of things that can exchange information between objects;

A big data DB storing a prediction model of electric energy usage and previous electric energy usage pattern information (big data); And

A server for managing the management of a multi-dwelling facility, comprising: predicting the use of electric energy based on a predictive model of use of electric energy stored in the big data DB and big data; A housing management server for receiving a set value and generating an electric energy use schedule to control electric use of the electric appliance; To

.

A remote meter reading unit for reading the total electricity usage amount of the individual house, recognizing the accumulated electricity usage amount information for a predetermined period of time, and transmitting the information to the remote meter reading server via the Internet,

And a remote meter reading server for collecting information from the remote meter, analyzing an electric energy usage pattern, and providing the analyzed information to a housing management server.

The system may further comprise a user terminal connected to the housing management server or the remote meter-reading server for confirming the electric energy usage information or resetting the electric use of the electric appliance of the customer.

Also, the housing management server learns a hidden layer so as to reconstruct the pattern shape through an RBM (Restricted Boltzmann Machine) algorithm. When only a part of the electric energy use pattern is given, A model capable of restoring the entire shape can be constructed to restore the entire pattern from the recent electric energy usage information to the future electric energy usage amount to predict the next electric energy use.

The housing management server sequentially inputs past electric energy usage information in a chronological order through an RNN (Recurrent Neural Network) algorithm, sequentially receives the inputs from the hidden layer, recovers the values received from the hidden layer in the previous step, In this model, the following electric energy usage is predicted, and the current hidden model value is recycled to the next stage. Then, the model is learned through past electric energy usage information, And estimates the use.

A method of controlling power consumption averaging for saving a coin house electricity bill according to an embodiment of the present invention includes the steps of: (a) collecting information on electric energy usage amount by households for a predetermined period of time through a remote meter and an object internet network;

(b) collecting electric energy usage amount information and attribute information for each electric energy use object (by the consumer electric appliance) from the housing management server through the consumer electric appliance and the object internet network,

(c) predicting the peak load based on the information collected in the steps (a) and (b) and the previous electrical energy usage pattern big data stored in the big data DB in the housing management server;

(d) determining whether the peak load value predicted in step (c) is greater than or equal to a preset peak load value;

(e) generating an electricity use schedule for each electric energy using object (per customer electric appliance) when the predicted peak load value is equal to or greater than a predetermined peak load value; And

(f) controlling electricity use by transmitting electricity use control data to the corresponding consumer electric equipment according to an electricity use schedule for each object in the housing management server; To

.

In the step (f), the electricity usage control data is transmitted from the housing management server to the user terminal so that the user can reset the electricity usage control data.

According to the solution of the above-mentioned problem, it is possible to increase the energy use efficiency by managing the maximum power amount of the apartment house and to manage the peak electric power, thereby reducing the basic charge of the electricity cost applied to the apartment house, Cognitive technology and its automatic control technology can optimize energy consumption, thus reducing energy consumption and optimizing energy consumption.

1 is a block diagram of a power consumption averaging control system according to an embodiment of the present invention.
2 is a flowchart illustrating a power consumption averaging control method according to an embodiment of the present invention.
3 is a conceptual diagram of peak control applied to the present invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings.

It is to be noted that the same components of the drawings are denoted by the same reference numerals and symbols as possible even if they are shown in different drawings.

In the following description of the present invention, a detailed description of known functions and configurations incorporated herein will be omitted when it may make the subject matter of the present invention rather unclear.

Also, when a part is referred to as " including " an element, it does not exclude other elements unless specifically stated otherwise.

1 is a block diagram of a power consumption averaging control system according to an embodiment of the present invention.

As shown in FIG. 1, a power consumption averaging control system according to an embodiment of the present invention includes: an electric appliance 12a, ..., 12n provided for each individual house 10a, ..., 10n; 14n, a housing management server 30, a remote meter reading server 40, a big data DB 50, and a user terminal 60. [

... and 12n of the individual houses 10a to 10n and the remote meter reading devices 14a to 14n are connected to the housing management server 30 and the remote meter reading server 40 and the object Internet IoT Network 20 as shown in FIG.

Each of the individual houses 10a to 10n includes a plurality of consumer electronic devices 12a to 12n and remote meter reading devices 14a to 14n .

(12a, ..., 12n) is a device that consumes electric energy, which is built in or attached to a thing Internet communication function, recognizes the amount of electric energy consumed by the device, and transmits it to a housing management server 30).

That is, the consumer electric appliances 12a, ..., 12n include a built-in object Internet communication and a sensor function.

The consumer electric appliances 10a, ..., 10n are, for example, an air conditioner, an electric heater, a washing machine, a refrigerator,

The object Internet 20 is a network capable of exchanging information between objects such as Wi-Fi, ZigBee, beacon, Bluetooth, and wired Internet.

The big data DB 50 is a database that stores a predictive model for use of electric energy and previous electric energy usage pattern information (big data), and is built in the housing management server 30 or the remote meter reading server 40 by software It is possible.

The housing management server 30 is a server that handles the management of the apartment housing facility. The housing management server 30 predicts the use of electric energy based on the model of the use of electric energy stored in the big data DB 40 and the big data, And overall electricity energy usage forecasts and pricing based on energy use.

Here, the prediction of electric energy use is as follows: First, hidden pattern is learned by reconstructing patterns through RBM (Restricted Boltzmann Machine) algorithm. By constructing a model that can restore the whole shape, it is possible to estimate the next electric energy use by restoring the whole pattern including recent electric energy usage information to the usage amount of future electric energy.

Secondly, the past electric energy usage information is sequentially input in chronological order through the RNN (Recurrent Neural Network) algorithm, and the inputs are sequentially received from the hidden layer, combined with the values recirculated in the previous hidden layer, And the current value of the hidden layer is cycled back to the next step. The model constructed in this way can be learned through past electric energy usage information, and the use of next electric energy can be predicted through this learned model.

At this time, each electric energy usage forecasting model can be learned separately for each individual, and the total power can be obtained by estimating each energy use.

The housing management server 30 also receives the electric energy usage amount from the consumer electric appliances 12a to 12n of the individual house and the electric energy consumption amount from the user terminal 60 through the object internet network 20, ) To generate electricity usage schedule.

The remote meter reading devices 14a to 14n meter the total electricity consumption of the individual houses 10a to 10n and recognize cumulative usage amount information for a certain period of time such as a unit of usage amount accumulated for 15 minutes, To the remote meter reading server (40) or the housing management server (30).

The remote meter reading server 40 collects information from the remote meter reading devices 14a to 14n which meter the total electricity consumption of the individual houses 10a to 10n and analyzes the electricity use patterns to thereby collect the information from the housing management server 30.

The user terminal 60 is connected to the housing management server 30 or the remote meter reading server 40 to allow the user to confirm the electric energy usage information or reset the electric use of the electric appliance of the customer, , A PC, or the like, which is capable of connecting to the Internet or the Internet.

FIG. 2 is a flowchart showing a power consumption averaging control method according to an embodiment of the present invention, and FIG. 3 is a conceptual diagram of peak control applied to the present invention.

As shown in FIG. 2, the power consumption averaging control method according to an embodiment of the present invention includes a step of collecting electric energy usage amount (S20) for each household (individual house), an electric energy usage amount collecting step (S22) Step S24, a peak load value over determination step S26, an electricity use schedule creation step S26, and an electricity use control step S30.

First, the electric energy usage collection step (S20) of each household (individual house) is performed by the housing management server 30 for the set electric energy usage amount per household for the set time through the remote meter reading devices 14a to 14n and the object internet network 20 And collecting information.

The step of collecting the electric energy usage by object S22 is performed by the housing management server 30 through the electric appliances 12a to 12n and the object internet 20 by electric energy use objects, Usage information, and attribute information (e.g., a receiver ID, an electric device type, and setting information).

The peak load prediction step S24 is a step of estimating a peak load based on the information collected in the steps S20 and S22 and the previous electric energy usage pattern big data stored in the big data DB 50 in the housing management server 30, .

Here, the peak load is the load of the time when the aggregate demand increases because various loads (loads) are overlapped in view of the power use situation of one day.

The peak load value over determination step S26 is a step of determining whether the peak load value predicted in the peak load prediction step S224 is greater than or equal to a preset peak load value.

In the step S28 of generating an electricity usage schedule for each object, a schedule for generating an electricity usage schedule for each electric energy using object (by each electric appliance) is generated according to the electric energy averaging schedule when it is determined to be equal to or greater than the set peak load value.

If it is determined that the peak load value is equal to or less than the set peak load value, the process returns to step S20 and performs the subsequent steps.

The electricity use control step S30 is a step in which the housing management server 30 controls electricity use by transmitting electricity use control data to the corresponding consumer electric appliances 12a, ..., 12n according to the per-object electricity usage schedule.

At this time, the electricity usage control data may be transmitted from the housing management server 30 to the user terminal 60 so that the user can confirm the electricity use control data and re-adjust the electricity use control data.

Through these steps, the electric power consumption can be averaged as shown in FIG.

Although the preferred embodiments of the present invention have been disclosed for illustrative purposes, those skilled in the art will appreciate that various modifications, additions and substitutions are possible, without departing from the scope and spirit of the invention as disclosed in the accompanying claims.

In addition, it is a matter of course that various modifications and variations are possible without departing from the scope of the technical idea of the present invention by anyone having ordinary skill in the art.

10a, ... , 10n: individual house 12a, ... , 12n: consumer electrical equipment
14a, ... , 14n: remote meter reading device 20:
30: Housing management server 40: Remote meter reading server
50: Big data DB 60: User terminal

Claims (7)

It is equipped with a number of individual homes that make up a multi-family house, and has a sensor function to recognize the Internet communication function of objects and the amount of electric energy consumed by oneself, so that electric energy usage information and property information A consumer electric appliance provided to a housing management server;
Internet of things that can exchange information between objects;
A big data DB storing a prediction model of electric energy usage and previous electric energy usage pattern information (big data);
A server that handles the management of a multi-family housing facility, predicts the use of electric energy in individual houses and apartment houses based on a predictive model of the use of electric energy stored in the big data DB and Big Data, A housing management server for receiving the electric energy usage amount from the server and the electricity use reset value from the user terminal to generate electric energy use schedules to control electric use of the electric appliance to reduce the maximum electric power of the apartment house to manage peak electric power;
A remote meter reading the total electricity usage amount of the individual house, recognizing the accumulated electricity usage information for a predetermined period, and transmitting the information to the remote meter reading server through the Internet of objects;
An automatic meter reading server for collecting electric energy usage information for each electric appliance from the remote meter, analyzing electric energy usage patterns and providing the electric energy usage pattern to a housing management server; And
A user terminal connected to the housing management server or the remote meter-reading server for confirming electric energy usage information, resetting the electric use of the consumer electric appliance, and transmitting the electric energy to the housing management server; / RTI >
The housing management server sequentially inputs past electric energy usage information through a RNN (Recurrent Neural Network) algorithm, sequentially receives input from the hidden layer, and combines it with a value recirculated in a previous hidden layer And the current hidden energy level is recycled back to the next step. The model is then learned through past electric energy usage information and the next electric energy is used Estimating the electric energy usage prediction model separately for each individual, predicting the use of each electric energy, and summing the total electric energy to obtain a total maximum electric power. delete delete delete delete delete delete

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