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.
CN113919600AActive Publication Date: 2022-01-11ECONOMIC & TECH RES INST OF HUBEI ELECTRIC POWER COMPANY SGCC
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
ECONOMIC & TECH RES INST OF HUBEI ELECTRIC POWER COMPANY SGCC
Publication Date
2022-01-11

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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.
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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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