Adaptive Feature Selection and Construction for Day-Ahead Load Forecasting Use Deep Learning Method

As a networked cyber physical system, smart grid plays an important role in improving energy efficiency, which has higher prediction accuracy requirement for load forecasting to realize intelligent dispatching. The existing works of load forecasting have considered the external factors to improve prediction accuracy, but ignored their varying and different impacts in different time. In order to solve this problem, firstly, a new method is designed to select external meteorological factors and construct new features to deal with the different impacts of external factors in different seasons. Secondly, a fuzzy processing method of calendar factor is proposed to explore the varying impact of the calendar factor in the holiday period. Finally, our proposed method is combined with LSTM and other three typical machine learning models, and experiments are performed on a load data set from the real power system. The results show that our proposed method can improve the prediction accuracy of the typical machine learning based models. Especially for LSTM, the MAPEs reach to 3.8% and 3.1% in winter and summer, with the improvements of 0.6% and 0.3% respectively.