Short-term Power Load Forecasting Based on Temporal Convolutional Network

Abstract

For the problem of short-term power load forecasting, a numerical algorithm is presented based on Temporal Convolutional Network (TCN) and XGBoost. Firstly, correlations between load and influencing factors are analyzed so as to extract important features by XGBoost. Secondly, residual blocks in TCN mainly deal with gradient disappearance or explosion for the long time series. Combined with attention mechanism, important feature vectors may be benefit to advance the accuracy of forecasting results. Short-term power load forecasting is carried out with multiple scales by two groups of public data. Compared with TCN, Gate Recurrent Unit (GRU) and Long Short-Term Memory (LSTM), the presented algorithm needs less time when high-frequency or high-dimensional data appears.