An Adaptive Ensemble Data Driven Approach for Nonparametric Probabilistic Forecasting of Electricity Load
Can Wan, Zhaojing Cao, Wei‐Jen Lee, Yonghua Song, Ping Ju
Abstract
Probabilistic load forecasting that provides uncertainty information involved in load forecasting is crucial for various decision-making tasks in power systems. This paper proposes a novel adaptive ensemble data driven (AEDD) approach for nonparametric probabilistic forecasting of electricity load by mining the uncertainty distribution from the historical observations based on conditional historical dataset construction and adaptive weighted ensemble. The pertinent patterns similar to the forecasting condition are searched from the numerous historical observations. The similarity degree measurement method is established based on shared nearest neighbors. Moreover, the uncertainty degree of the predictive load is quantified via information entropy, and then the number of similar patterns is determined depending to its uncertainty degree. After obtaining the conditional historical dataset, an adaptive weighted ensemble method is proposed for estimating the uncertainty distribution more correctly, where the weight for each similar pattern is set based on its similarity degree with the predictive load. Comprehensive numerical studies based on realistic load data validate the superiority of the proposed AEDD method in both accuracy and computational efficiency.