Load prediction method based on dynamic time warping and long-short time memory

Abstract

The invention discloses a load prediction method based on dynamic time warping and long-short time memory, and the method comprises the following steps: S1, obtaining the basic data required for short-term load prediction of a user from a power system; S2, carrying out the clustering of users with similar power utilization behaviors through employing a dynamic time warping method according to thehistorical load data of the user; S3, performing pooling processing on the user data of the same category; S4, selecting training data, preprocessing the training data and using the preprocessed training data as input; and S5, constructing a short-term load prediction method based on the deep long-term and short-term memory recurrent neural network, and verifying the effectiveness. According to the method, the users with similar electricity consumption behaviors are clustered according to the characteristic of large cardinal number of the to-be-predicted users, so that the prediction efficiency is improved. Meanwhile, through pooling processing on the data in the same category, the diversity of the training data is increased, the short-term load prediction precision is improved, and certain engineering application significance is achieved.

Description

technical field [0001] The invention relates to a short-term load forecasting method for electric power system residents, in particular to a short-term load forecasting method for electric power system residents, which is used for predicting electric power system load and belongs to the technical field of pattern recognition and image processing. Background technique [0002] Residential load forecasting in the power system is based on the historical load change law, combined with meteorological, economic and other factors to scientifically predict the load for the next few days or hours. Accurate load forecasting is an important decision-making basis for arranging power production scheduling and equipment maintenance planning. Therefore, it is necessary to study new methods and new technologies for residents' load forecasting in order to improve the accuracy and reliability of load forecasting and meet the requirements of engineering technology. [0003] In recent years, w...

AI Technical Summary

Problems solved by technology

A load profile is a 24-hour record of a household's energy consumption, and while consumer behavior indicates device usage patterns, the exact time a device is used can vary, resulting in differently shaped load profiles

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