Research and Analysis of Short-term Load Forecasting Based on Frequency Domain Decomposition

doi:10.11930/j.issn.1004-9649.201809089

Abstract

Abstract: To study the effect of frequency domain component prediction on the accuracy of short-term load forecasting. This paper proposes to decompose the original load data based on the frequency domain decomposition algorithm,and decompose the data into four parts: daily cycle, weekly cycle, low frequency and high frequency components. Among them, the daily and weekly cycle components are forecasted by Elman neural network; the low-frequency components are forecasted by random forest; the high-frequency components are secondarily decomposed using the Mallat algorithm to obtain the low-frequency part and the high-frequency part respectively. The low-frequency part is selected as the training sample and is combined with the Elman neural network to predict the high-frequency components. The results of each frequency domain component are recombined to achieve high-precision prediction of power load. The actual load data of a certain city in Anhui Province is taken as an example. Compared with the forecasting results of Elman neural network, random forest method and frequency domain component forecasting method, the proposed method can effectively improve the accuracy and reduce the degree of dispersion of predicted and true values.

Key words: load forecasting, frequency domain decomposition, Elman neural network, random forest, Mallat algorithm

MA Yuan, ZHANG Qian, LI Guoli, MA Jinhui, DING Jinjin. Research and Analysis of Short-term Load Forecasting Based on Frequency Domain Decomposition[J]. Electric Power, 2020, 53(4): 114-121.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.201809089

https://www.electricpower.com.cn/EN/Y2020/V53/I4/114

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