电力系统及其自动化学报2016，Vol.28Issue(11)：9-13,5.DOI:10.3969/j.issn.1003-8930.2016.11.002
基于改进支持向量机算法的光伏发电短期功率滚动预测
Short-term Photovoltaic Power Generation Rolling Forecast Based on Optimized SVM
摘要
Abstract
Considering the volatility and randomness of photovoltaic(PV)generation systems,short-term forecast of PV power output can accurately achieve the grid scheduling and energy management. This paper proposes a rolling pre⁃diction model based on support vector machine optimized by particle swarm optimization(PSO-SVM). Through finding out a similar day to the predicted day,the power output of the similar day and the weather data of the predicted day are taken as the input of the model to forecast the power of the next day. Then,the forecasted power data and actual power of the next day are compared. If the forecasted power cannot satisfy the given forecast accuracy,then the actual power is used to revise the forecasted power. Simulation result shows that the rolling forecast model of the short-term PV power can accurately forecast the power output of PV system.关键词
光伏发电/短期功率预测/粒子群优化/支持向量机/滚动预测Key words
photovoltaic(PV)power generation/short-term power forecast/particle swarm optimization(PSO)/sup-port vector machine(SVM)/rolling forecast分类
信息技术与安全科学引用本文复制引用
王继东,宋智林,冉冉..基于改进支持向量机算法的光伏发电短期功率滚动预测[J].电力系统及其自动化学报,2016,28(11):9-13,5.基金项目
国家自然科学基金资助项目 （）
