Electricity load forecasting is of high importance for electricity management. Modern power systems are complex and diverse, resulting in increased randomness and nonlinear factors of electricity load data, which greatly increases the difficulty of forecasting. This paper proposes a hybrid-deep-learning-based load forecasting method, named DCFformer (DFT-CNN-FEDformer), for short-term load forecasting (STLF) tasks. The method first employs the discrete Fourier transform (DFT) to denoise time-sequence data on electricity load, so that fluctuations caused by incidents can be reduced. Secondly, it utilizes a convolutional neural network (CNN) that produces sequences of local features extracted from the denoised time sequences. Thirdly, a FEDformer network is applied to perform load forecasting by using extracted feature sequences. In the experiments, we utilize datasets from three regional power systems or apparatuses to compare the proposed DCFformer with other approaches, and the results show that, under the same conditions, DCFformer outperforms the competitors in forecasting precision, which proves the significance of its performance and practicality.
Huang et al. (Sat,) studied this question.
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