Resident load ultra-short-term prediction method

An ultra-short-term forecasting and load technology, applied in forecasting, instruments, biological neural network models, etc., can solve the problem of low load forecasting accuracy, and achieve the effect of improving clustering effect, improving forecasting accuracy, and reducing complexity.

Active Publication Date: 2022-01-11
ECONOMIC & TECH RES INST OF HUBEI ELECTRIC POWER COMPANY SGCC
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Problems solved by technology

[0005] The purpose of the present invention is to overcome the defects and problems of low load forecasting accuracy existing in the prior art, and provide a super-short-term load forecasting method for residents with high load forecasting accuracy

Method used

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  • Resident load ultra-short-term prediction method

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Embodiment

[0142] see figure 1 , a method for ultra-short-term forecasting of residential load, the method includes the following steps:

[0143] S1. Obtain residents' electricity load through smart meters and other electricity monitoring equipment Daily historical electricity consumption data, Taking 90, the power consumption monitoring equipment collects power consumption every 15 minutes, forming 96 periods of power consumption data every day, based on the ( Take 6, date The electricity consumption data of a period reflects the initial characteristics of the whole day's electricity load law, which can roughly represent the electricity consumption habit of the whole day) The daily electricity consumption data of a period is clustered and analyzed to obtain The load power consumption data of the same category as the day to be measured is the similar day category, which is denoted as ;

[0144] S11. A few days ago The daily electricity consumption data for each time period...

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Abstract

A resident load ultra-short-term prediction method comprises the following steps: S1, acquiring daily historical electricity consumption data of resident electricity consumption loads, performing clustering analysis based on daily electricity consumption data of a day-ahead time period to obtain class load electricity consumption data, S2, selecting load power consumption data of the same category as the day to be measured, and performing wavelet decomposition to obtain three components; and S3, training the sum component by using LSTM to respectively obtain prediction results of the three components of the day to be measured, and superposing the prediction results of the sum component and the three components to obtain a prediction result of the day to be measured. According to the design, the precision of ultra-short-term prediction of the power consumption load of a single household is effectively improved.

Description

technical field [0001] The invention relates to the technical field of distribution network load forecasting, in particular to a method for ultra-short-term forecasting of residential loads, which is mainly suitable for improving the accuracy of load forecasting. Background technique [0002] Traditional load forecasting mainly includes two methods. One is the data-driven power load forecasting method, which uses various advanced machine learning algorithms to realize power load forecasting on the day to be forecasted. This type of method can be implemented based on a single machine learning algorithm, or based on a combination of multiple machine learning algorithms. In order to improve the forecasting accuracy, with the advantages of each load forecasting algorithm, the load forecasting method based on a variety of machine learning combination algorithms has been greatly developed. For example, some methods use the back propagation (BP) neural network to analyze the histo...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06N3/04G06K9/62
CPCG06Q10/04G06Q50/06G06N3/044G06F18/23G06F18/214
Inventor 侯婷婷方仍存杨东俊颜玉林张维唐金锐汪致洵贺兰菲雷何杨洁桑子夏
Owner ECONOMIC & TECH RES INST OF HUBEI ELECTRIC POWER COMPANY SGCC
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