User-Level Ultra-Short-Term Load Forecasting Model Based on Optimal Feature Selection and Bahdanau Attention Mechanism

Abstract

Accurate ultra-short-term load forecasting is of great significance for real-time power generation scheduling and development of power cyber physical systems (Power CPS). However, in order to forecast the future load using the current high-dimensional, diverse and heterogeneous electric power consumption information, new challenges have been raised to the effective feature selection and the accurate load forecasting algorithms. However, very limited existing works consider the feature selection for the electric power consumption information and impacts to the thereafter load forecasting model. In view of this point, features that are critical to the load forecasting are selected using an embedded feature selection algorithm based on LightGBM to form an optimal feature set, with which a sequence to sequence (S2S) and gated recurrent unit (GRU)-based ultra-short-term load forecasting model that incorporates Bahdanau attention (BA) mechanism is presented. The S2S-GRU model is based on an encoding–decoding framework that is compatible to the input and output data series with variable lengths. By introducing the BA mechanism, loss of previous information issue of GRU can be solved. Experimental results show that first the presented feature selection algorithm can help to improve the performance of the load forecasting model. Second, the presented load forecasting model can find a compromise between the forecasting efficiency and accuracy.