Day-Ahead Peak Load Probability Density Forecasting Based on QRLSTM-DF Considering Exogenous Factors

Abstract

High-precision peak load forecasting is the guarantee of dispatching and decision-making in power systems. It is difficult to predict the peak power demand accurately and robustly due to many unstable factors that affect power loads. In order to solve this problem, this paper proposes quantile regression long-short term memory based on decoupling features (QRLSTM-DF) for probability density forecasting of the day-ahead peak load. This method reduces the possibility of mutual influence among characteristics by decoupling factors in different branches of neural network. Then, kernel density estimation (KDE) is used as a post-processing technique to generate probability density curves, which can give the comprehensive probability distribution of future peak loads and effectively quantify the uncertainty. Experimental results on three datasets show that the model outperforms several existing prediction models. In particular, the prediction results of the maximum daily peak power load and the peak load beyond the sample maximum boundary are also accurate and robust.