KR20190126527A - Apparatus and method for predicting energy use and generation through self-enhancement learning - Google Patents

Abstract

Disclosed are a device for predicting an energy use amount and a power generation amount through self-enhancement learning and a method thereof. The method for predicting an energy use amount and a power generation amount comprises the steps of: confirming whether omission data exists in input data and processing the omission data when the omission data exists in the input data; retrieving data related to a target for predicting an energy use amount and an energy power generation amount from the input data in which the omission data is processed; synchronizing the retrieved data in accordance with a time-based point or a space-based point; labeling the synchronized data; filtering the labeled data to extract data corresponding to the target; and analyzing the extracted data to predict the energy use amount and the energy power generation amount of the target.

Description

Apparatus and Method for Predicting Energy Consumption and Power Generation through Self-Advanced Learning

The present invention relates to an apparatus and method for predicting energy usage and solar power generation using self-enhanced learning.

Renewable energy, including solar energy, has the fatal drawback that energy is generated intermittently and the amount available is limited. This is a fatal drawback when compared to fossil fuel generation, which was always available when needed.

Solar energy, for example, limits its use because only the remaining energy from solar cells during the day can be used at night. In addition, because of the intermittent energy in the cloudy weather, the use is more limited. In order to compensate for these disadvantages, it is necessary to accurately predict the future solar energy and to use the solar energy efficiently according to the prediction result.

Conventional methods for predicting solar power generation use Regressive, ANN, and SVR. For example, the hybrid-statistical method of the conventional method of predicting the amount of photovoltaic power generation is a method of predicting the amount of power generation using a plurality of different machine learning techniques. In addition, Hybrid-physical predicts photovoltaic power generation using a combination of physical methods using the physical configuration of the solar system and prediction using machine learning.

Since the more accurate the prediction of the amount of photovoltaic power generation and the amount of energy used, the more efficient use of the photovoltaic energy is required.

The present invention can provide an apparatus and method for predicting energy usage and power generation.

In addition, the present invention can provide a device and method that can increase the accuracy of the analysis results by knowledge of the prediction results.

Energy consumption and generation amount prediction method according to an embodiment of the present invention comprises the steps of checking whether there is missing data in the input data, and if there is missing data, processing the missing data; Retrieving data related to the energy usage and the target for predicting the amount of energy generation from the input data processed by the missing data; Synchronizing the retrieved data according to a temporal reference point or a spatial reference point; Labeling the synchronized data; Filtering the labeled data to extract data corresponding to the target; And analyzing the extracted data to predict energy usage and energy generation amount of the target.

According to an embodiment of the present invention, the measurement data collected from the energy devices and sensors and the knowledge data collected from the big data platform may be preprocessed and analyzed to predict energy usage and power generation.

In addition, according to an embodiment of the present invention, by predicting and storing the prediction result, when the energy consumption and power generation amount are predicted in the future, the accuracy of the analysis result can be improved.

1 is a view showing an energy consumption and generation amount prediction apparatus according to an embodiment of the present invention.
2 is an example of database servers according to an embodiment of the present invention.
3 is a flowchart illustrating a method of predicting energy consumption and power generation amount according to an embodiment of the present invention.
4 is a flowchart illustrating a data preprocessing process of an energy consumption and generation amount prediction method according to an embodiment of the present invention.

Hereinafter, exemplary embodiments will be described in detail with reference to the accompanying drawings. However, various changes may be made to the embodiments so that the scope of the patent application is not limited or limited by these embodiments. It is to be understood that all changes, equivalents, and substitutes for the embodiments are included in the scope of rights.

The terminology used herein is for the purpose of description and should not be construed as limiting. Singular expressions include plural expressions unless the context clearly indicates otherwise. In this specification, terms such as "comprise" or "have" are intended to indicate that there is a feature, number, step, action, component, part, or combination thereof described on the specification, and one or more other features. It is to be understood that the present invention does not exclude the possibility of the presence or the addition of numbers, steps, operations, components, components, or a combination thereof.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art. Terms such as those defined in the commonly used dictionaries should be construed as having meanings consistent with the meanings in the context of the related art and shall not be construed in ideal or excessively formal meanings unless expressly defined in this application. Do not.

In addition, in the description with reference to the accompanying drawings, the same components regardless of reference numerals will be given the same reference numerals and duplicate description thereof will be omitted. In the following description of the embodiment, when it is determined that the detailed description of the related known technology may unnecessarily obscure the gist of the embodiment, the detailed description thereof will be omitted.

Energy consumption and generation amount prediction method according to an embodiment of the present invention may be performed by the energy consumption and generation amount prediction apparatus.

1 is a view showing an energy consumption and generation amount prediction apparatus according to an embodiment of the present invention.

The energy usage and generation amount prediction apparatus 100 may include a data collector 110, a data preprocessor 120, and a data analyzer 130 as shown in FIG. 1. In this case, the data collection unit 110 may be a communication device, and the data preprocessor 120 and the data analyzer 130 may be respective modules included in different processes or programs executed in one process.

The data collector 110 may retrieve and collect input data from at least one of the sensor 101, the meteorological office server 102, the Internet 103, and the database server 140. In this case, the input data collected by the data collector 110 may include current measurement data and state data collected from the device or sensor 101, climate data related to weather collected from the Meteorological Agency server 102, and social data. It may include at least one of context data collected through the media and the Internet 103, policy data collected from the database server 140, and knowledge data.

In this case, the measurement data and the state data may be data measured at the present time by the sensor 101 capable of measuring data related to the amount of photovoltaic power generation. In addition, the contextual data may include regional data related to regional locations. The policy data may include power unit price, facility cost, and depreciation cost. In addition, the knowledge data may include a result accumulated and analyzed from the time point at which the energy consumption and power generation amount prediction apparatus 100 starts to operate to the present.

The data collector 110 may independently collect data from the sensor 101, the meteorological office server 102, the Internet 103, and the database server 140, respectively. In addition, the data collector 110 operates independently of the operations of the data preprocessor 120 and the data analyzer 130, and may collect data in real time or at predetermined time intervals.

The data preprocessor 120 may preprocess the data collected by the data collector 110 so that the data analyzer 130 may analyze the data. In this case, the data preprocessor 120 may check whether there is missing data in the input data. If there is missing data, the data preprocessor 120 may process the missing data.

In addition, the data preprocessor 120 may search for data related to a target for predicting the energy usage and the amount of energy generation from the input data in which the missing data is processed. Next, the data preprocessor 120 may synchronize the retrieved data according to a time reference point or a spatial reference point. Next, the data preprocessor 120 may label the synchronized data. Finally, the data preprocessor 120 may extract the data corresponding to the target by filtering the labeled data.

The data analyzer 130 may analyze the data preprocessed by the data preprocessor 120 to predict energy usage and energy generation of the target. In this case, the data analyzed by the data analyzer 130 may be data corresponding to the target extracted by filtering the labeled data.

In addition, the data analyzer 130 may provide a visualization service by visualizing the analysis result of the data. In addition, the data analysis unit 130 may store and manage the analysis result and the data visualized in the database server 140 through a knowledge technique.

The database server 140 may be a big data platform that receives, stores, and manages analysis results from the energy usage and generation amount prediction devices 100 that predict energy usage and generation amounts in different regions.

In addition, the database server 140 may include an energy consumption DB (Database) server 141, an energy production DB server 142, a cost DB server 143, and a policy DB server 144 as shown in FIG. 1. have. The detailed configuration of the database server 140 will be described in detail with reference to FIG. 2 below.

The energy consumption and generation amount prediction device 100 may predict the energy consumption and generation amount by preprocessing and analyzing the measurement data collected from the energy device and the sensor and the knowledge data collected from the big data platform. In addition, the energy consumption and generation amount prediction apparatus 100 may store the knowledge of the prediction result by knowledge, and when the energy consumption and generation amount is predicted in the future, the accuracy of the analysis result may be increased.

2 is an example of database servers according to an embodiment of the present invention.

The energy consumption DB server 141 may store and manage data related to energy consumption prediction.

The energy production DB server 142 may store and manage data related to power generation prediction of each of various power generation devices such as renewable energy.

The cost DB server 143 may store and manage data related to costs for producing energy in a power generation device, costs related to the consumption of energy, and benefits related to energy production and consumption.

The policy DB server 144 may store and manage data related to government policies and current energy technology trends.

As shown in FIG. 2, the energy consumption DB server 141, the energy production DB server 142, the cost DB server 143, and the policy DB server 144 respectively include trend data including future prediction data and similarities in the past. Pattern data including trends, representative algorithm results, and analysis data received from the energy consumption and generation amount prediction device 100 and raw data including raw data may be included.

The data stored in the energy consumption DB server 141, the energy production DB server 142, the cost DB server 143, and the policy DB server 144 may be stored in the energy consumption and power generation prediction device 100 by the sensor 101, the Meteorological Agency The data collected in real time from the server 102 and the Internet 103 may be used again in the preprocessing process to increase the accuracy of the data analysis process.

3 is a flowchart illustrating a method of predicting energy consumption and power generation amount according to an embodiment of the present invention.

In operation 310, the data collector 110 may retrieve and collect input data from at least one of the sensor 101, the meteorological office server 102, the Internet 103, and the database server 140.

In operation 320, the data preprocessor 120 may preprocess the data collected in operation 310 so that the data analyzer 130 may analyze the data. In this case, the data preprocessor 120 may check whether there is missing data in the input data. If there is missing data, the data preprocessor 120 may process the missing data.

In operation 330, the data analyzer 130 may analyze the data preprocessed in operation 320 to predict the energy consumption and the energy generation amount of the target.

In operation 340, the data analyzer 130 may provide a visualization service by visualizing an analysis result of the data predicted in operation 330.

In operation 350, the data analysis unit 130 may store the visualization result of the analysis result and the analysis result in the database server 140 to learn the analysis result and the visualized data.

4 is a flowchart illustrating a data preprocessing process of an energy consumption and generation amount prediction method according to an embodiment of the present invention. Steps 410 to 450 of FIG. 4 may be included in step 320 of FIG. 3.

In operation 410, the data preprocessor 120 may determine whether there is missing data in the input data and, if there is missing data, process the missing data.

In detail, the data preprocessor 120 may perform an engraving process of generating data fields by storing data collected by the data collector 110. In this case, the data preprocessor 120 may check and process data missing or missing from the input data. For example, when missing data occurs in the metering data, the data preprocessor 120 may process the average value of the data adjacent to the missing data as the value of the missing data. In addition, when missing data occurs in the state data, the data preprocessor 120 may replace the data of the previous time adjacent to the missing data or the data of the subsequent time adjacent to the missing data with a value of the missing data.

In operation 420, the data preprocessor 120 may search for data related to a target for predicting an energy consumption and an amount of energy generation, from the input data in which missing data is processed in operation 410. In this case, the data searched by the data preprocessor 120 may include data related to the region where the target is installed.

In operation 430, the data preprocessor 120 may synchronize the data retrieved in operation 420 according to a temporal reference point or a spatial reference point.

Data collected through an independent energy device or sensor 101 need time synchronization. However, since global time synchronization is difficult due to time difference, the data preprocessor 120 may synchronize data based on an event. In addition, the data synchronized based on the event may have a correlation.

Therefore, the data preprocessor 120 may set reference points of time series data and spatial data according to energy events. For example, the data preprocessor 120 may set events such as explosive energy usage or energy device failure and climate change as reference points of time series data and spatial data.

In operation 440, the data preprocessor 120 may label the data synchronized in operation 430 to give the data meaning of an event.

In operation 450, the data preprocessor 120 may extract data corresponding to the target by filtering the data labeled in operation 440. In this case, the data preprocessor 120 may extract data corresponding to the analysis purpose from the labeled data.

The present invention can pre-process and analyze the measurement data collected from the energy devices and sensors and the knowledge data collected from the big data platform to predict energy usage and power generation. In addition, according to the present invention, the prediction result is stored in a knowledgeable manner, and when the energy consumption and power generation amount are predicted in the future, the accuracy of the analysis result can be improved.

Method according to the embodiment is implemented in the form of program instructions that can be executed by various computer means may be recorded on a computer readable medium. The computer readable medium may include program instructions, data files, data structures, and the like, alone or in combination. The program instructions recorded on the media may be those specially designed and constructed for the purposes of the embodiments, or they may be of the kind well-known and available to those having skill in the computer software arts. Examples of computer readable recording media include magnetic media such as hard disks, floppy disks and magnetic tape, optical media such as CD-ROMs, DVDs, and magnetic disks such as floppy disks. Magneto-optical media, and hardware devices specifically configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include machine code, such as produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like. The hardware device described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

The software may include a computer program, code, instructions, or a combination of one or more of the above, and configure the processing device to operate as desired, or process independently or collectively. You can command the device. Software and / or data may be any type of machine, component, physical device, virtual equipment, computer storage medium or device in order to be interpreted by or to provide instructions or data to the processing device. Or may be permanently or temporarily embodied in a signal wave to be transmitted. The software may be distributed over networked computer systems so that they are stored or executed in a distributed manner. Software and data may be stored on one or more computer readable recording media.

Although the embodiments have been described with reference to the accompanying drawings, those skilled in the art may apply various technical modifications and variations based on the above. For example, the described techniques may be performed in a different order than the described method, and / or components of the described systems, structures, devices, circuits, etc. may be combined or combined in a different form than the described method, or other components. Or even if replaced or substituted by equivalents, an appropriate result can be achieved.

Therefore, other implementations, other embodiments, and equivalents to the claims are within the scope of the following claims.

110: data collector
120: data preprocessor
130: data analysis unit

Claims (1)

Checking whether there is missing data in the input data, and processing missing data if there is missing data;
Retrieving data related to the energy usage and the target for predicting the amount of energy generation from the input data processed by the missing data;
Synchronizing the retrieved data according to a temporal reference point or a spatial reference point;
Labeling the synchronized data;
Filtering the labeled data to extract data corresponding to the target; And
Analyzing the extracted data to predict the energy usage and energy generation amount of the target
Energy usage and generation amount prediction method comprising a.

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