Short-Term Load Forecasting Based on Frequency Domain Decomposition and Deep Learning

Zhang Qian; Ma Yuan; Li GuoliMa JinhuiDing Jinjin

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Short-Term Load Forecasting Based on Frequency Domain Decomposition and Deep Learning

机译：Short-Term Load Forecasting Based on Frequency Domain Decomposition and Deep Learning

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In this paper, we focus on the accuracy improvement of short-term load forecasting, which is useful in the reasonable planning and stable operation of the system in advance. For this purpose, a short-term load forecasting model based on frequency domain decomposition and deep learning is proposed. The original load data are decomposed into four parts as the daily and weekly periodic components and the low- and high-frequency components. Long short-term memory (LSTM) neural network is applied in the forecasting for the daily periodic, weekly periodic, and low-frequency components. The combination of isolation forest (iForest) and Mallat with the LSTM method is constructed in forecasting the high-frequency part. Finally, the four parts of the forecasting results are added together. The actual load data of a Chinese city are researched. Compared with the forecasting results of empirical mode decomposition- (EMD-) LSTM, LSTM, and recurrent neural network (RNN) methods, the proposed method can effectively improve the accuracy and reduce the degree of dispersion of forecasting and actual values.

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《Mathematical Problems in Engineering: Theory, Methods and Applications》 |2020年第5期|7240320.1-7240320.9|共9页
作者
Zhang Qian; Ma Yuan; Li GuoliMa JinhuiDing Jinjin;
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作者单位

Anhui Univ, Sch Elect Engn & Automat, Hefei, Peoples R China|Anhui Univ, Collaborat Innovat Ctr Ind Energy Saving & Power, Hefei, Peoples R China;

Anhui Univ, Sch Elect Engn & Automat, Hefei, Peoples R China;

Anhui Univ, Collaborat Innovat Ctr Ind Energy Saving & Power, Hefei, Peoples R ChinaState Grid Anhui Elect Power Co, Hefei, Peoples R ChinaAnhui Elect Power Res Inst, Hefei, Peoples R China;

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Short-Term Load Forecasting Based on Frequency Domain Decomposition and Deep Learning

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