Probabilistic Wind Generation Forecast Based on Sparse Bayesian Classification and Dempster–Shafer Theory

Abstract

Probabilistic wind generation forecast results are crucial for power system operational dispatch. In this paper, a nonparametric approach for short-term probabilistic wind generation forecast based on the sparse Bayesian classification (SBC) and Dempster–Shafer theory (DST) is proposed. This approach is composed of the following four steps. 1) A spot forecast of wind generation is performed based on support vector machine (SVM). 2) The range of SVM forecast error is discretized into multiple intervals, and the conditional probability of each interval is estimated by a sparse Bayesian classifier. 3) DST is applied to combine the probabilities of all the intervals to form a unified probability distribution function (pdf) of the SVM forecast error. 4) The pdf of wind generation is achieved by combining the SVM wind generation spot forecast result and corresponding forecast error distribution. The distinguishing features of the proposed approach are as follows. 1) The approach is a nonparametric one and the forecast error caused by the misjudgement of probability distribution type can be avoided. 2) The proposed approach has good generalization capability by using the sparse learning mechanism. 3) The range constraint of wind generation can be systematically considered in the approach by applying DST. Tests on a 74-MW wind farm illustrate the improvement of spot forecast accuracy of the proposed approach and validate that this paper provides better calibrated and sharper probabilistic forecasts than the empirical approach and quantile regression model. Comparison to the state-of-the-art of the Global Energy Forecasting Competition 2014 demonstrates the probabilistic forecast performance of the proposed approach.