Abstract
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.
Highlights
In order to meet the needs of rapid social development, the power system has gradually turned into a self-healing, largescale renewable energy access, economic, and efficient smart grid
In [11], the Long short-term memory (LSTM) neural network method is used in 48 nonresidential consumers’ energy consumption data in China. e problem of trapped in local minimum and disappeared gradient in the traditional neural network method during training is effectively solved by the LSTM neural network, which is beneficial to improving the accuracy of load forecasting
After the four parts are obtained as the daily periodic, the weekly periodic, the lowfrequency and the high-frequency components, different forecasting methods will be adopted according to the characteristics of different component sequences
Summary
In order to meet the needs of rapid social development, the power system has gradually turned into a self-healing, largescale renewable energy access, economic, and efficient smart grid. E problem of trapped in local minimum and disappeared gradient in the traditional neural network method during training is effectively solved by the LSTM neural network, which is beneficial to improving the accuracy of load forecasting. Methods for decomposing and reforecasting load sequences have become hot topics, such as EMD [18, 19], sparse decomposition [20], local mean decomposition [21], and frequency domain decomposition [22, 23]. Frequency domain decomposition method is introduced based on the periodicity of the load sequence. The field data of a Chinese city are decomposed based on the frequency domain decomposition algorithm to obtain four parts as the daily and weekly periodic components and the low- and the high-frequency components. Low-frequency signal is used as training samples, combined with the LSTM algorithm to forecasting the original high-frequency component.
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