Multi-Feature Integration Neural Network with Two-Stage Training for Short-Term Load Forecasting

Accurate short-term load forecasting (STLF) helps the power sector conduct generation and transmission efficiently, maintain stable grid operation while reducing energy waste, and thus achieve sustainable development. However, short-term load forecasting suffers from complex temporal dynamics and many environment variables, which causes considerable difficulties for the power sector. Therefore, short-term load forecasting is an essential yet challenging task. In this paper, we propose a short-term load forecasting model that integrates historical load, environment variables and temporal information, named TCN-GRU-TEmb. Our method utilizes temporal convolutional network (TCN) to capture the regularity of historical loads and gated recurrent unit (GRU) to extract useful features from environmental variables. As to temporal information, we propose a temporal embedding (TEmb) self-learning module, which can automatically capture the power consumption patterns of different time periods. We further propose a two-stage training algorithm to facilitate model convergence. Comparison experiments show that our model outperforms all the baselines, exhibiting an average reduction in MAE, MAPE, and RMSE of 8.24%, 9.23%, and 7.48%, respectively. Another experiment proves the effectiveness of the proposed temporal embedding method and two-stage training algorithm.