Short-Term Probability Density Function Forecasting of Industrial Loads Based on ConvLSTM-MDN - Wang, Yuan Y.; Wang, Ting Y.; Chen, Xiao Q.; Zeng, Xiang J.; Huang, Jing J.; Tang, Xia F. (Frontiers in Energy Research, 2022)
Load forecasting for industrial customers is essential for reliable operation decisions in electric power industry. However, most of the load forecasting literature has been focused on deterministic load forecasting (DLF), without considering information on the uncertainty of industrial load. This paper proposes a probabilistic density load forecasting model comprising convolutional long short-term memory (ConvLSTM) and mixture density network (MDN). First, a sliding window strategy is adopted to convert one-dimensional (1-D) data to two-dimensional (2-D) matrices to reconstruct input features. Then, the ConvLSTM is utilized to capture the deep information of the input features. At last, the MDN capable of directly predicting probability density functions (PDFs) of loads is adopted. Experimental results on the load datasets of three different industries show the accuracy and reliability of the proposed method.