A Large-Scale Multiobjective Evolutionary Quantile Estimation Model for Wind Power Probabilistic Forecasting

Abstract

Short-term wind power probabilistic forecasting can furnish decision-makers with comprehensive information to enhance management capabilities. Most wind power probabilistic predictions are modeled by multiple training of the pinball loss at a single quantile. However, this modeling leads to two underlying limitations, i.e., traditional probabilistic forecasting models fail to achieve a balance between the accuracy and width and are prone to quantile crossover. This article proposes a novel model called large-scale multiobjective evolutionary quantile estimation (LMOEQE) to obtain high-quality wind power probabilistic estimations. Specifically, for avoiding quantile crossover, a multiquantile regression monotone fuzzy neural network (MQRMFNN) is first proposed to simultaneously output monotonically increasing probability distributions. Then, a multiple loss function framework involving the accuracy, reliability and width is designed. Based on this framework, we regard the training of MQRMFNN as a large-scale multiobjective problem (MOP) to achieve the tradeoff on each metric of the probability distribution. But optimizing large-scale MOP for probabilistic neural network is extremely demanding in terms of efficiency and performance. A large-scale distributed multiobjective competitive swarm optimizer (LDMOCSO) is proposed for solving the constructed large-scale MOP. It implements a distributed competitive update strategy of different states to leverage global information from the decision space, effectively enhancing the convergence speed and diversity. All the methods show the superiority in real-world datasets.