关键词: 快速傅立叶变换, 残差网络, 短期电力负荷预测, 卷积神经网络, 时间序列

Abstract: Power industry requires accurate short-term load forecasting to provide precise load requirements for power system control and scheduling. In order to improve the accuracy of short-term power load forecasting, a method based on FFT optimized ResNet model is proposed. The model first defines power load forecasting as a time series problem, then introduces one-dimensional ResNet for power load regression prediction, and proposes to use FFT to optimize ResNet, the FFT transform of a layer of convolution results gives the model the ability to extract periodic features in the data. Experiments show that the prediction accuracy of FFT-ResNet is better than several benchmark models in 6-hour power load forecasting, which indicates that this method has a good application prospect in power load forecasting.

Key words: Fast Fourier Transform, residual network, short-term electric load forecasting, convolutional neural network, time series