电网技术2016，Vol.40Issue(4)：1113-1119,7.DOI:10.13335/j.1000-3673.pst.2016.04.020
基于条件分类与证据理论的短期风电功率非参数概率预测方法
Nonparametric Approach for Short-Term Probabilistic Wind Generation Forecast Based on Conditional Classification and Evidence Theory
摘要
Abstract
This paper proposes a nonparametric approach for probabilistic wind generation forecast based on sparse Bayesian classification (SBC) and Dempster-Shafer (D-S) theory. Forecast time horizon is 48 hours. Firstly, the approach makes a spot forecast of wind generation based on Support Vector Machine (SVM). Then, SVM forecast error range is discretized into multiple intervals, and conditional probability of each pre-designed interval is estimated by building a sparse Bayesian classifier. Thirdly, D-S theory isapplied to combine probabilities of all intervals to form a unified probability distribution function (PDF) of SVM forecast error. Finally, forecast result is obtained by superposition of SVM forecast result over mean value of forecasted error. The approach built on sparse Bayesian framework has high sparseness, ensuring its generalization ability and computation speed. Boundary constraint that wind generation should be within [0,GN] with installed capacityGNof wind farms, is taken into account, making forecast results well in line with actual results. Tests on a 74 MW wind farm illustrate effectiveness of the approach.关键词
风电功率概率预测/非参数估计/支持向量机/稀疏贝叶斯分类/D-S证据理论Key words
probabilistic wind generation forecast/non-parametric estimation/support vector machine/sparse Bayesian classification/Dempster-Shafer theory分类
信息技术与安全科学引用本文复制引用
林优,杨明,韩学山,安滨..基于条件分类与证据理论的短期风电功率非参数概率预测方法[J].电网技术,2016,40(4):1113-1119,7.基金项目
国家重点基础研究发展计划项目(973项目)(2013CB228205)；国家自然科学基金项目(51007047，51477091)。TheNational Basic Research Program of China (973 Program)(2013CB228205) （973项目）
Project Supported by National Natural Science Foundation of China (51007047,51477091) （51007047,51477091）
