水力发电2024，Vol.50Issue(4)：87-94,8.
基于CEEMD-SSA-SVM的短期光伏发电功率预测
Short-term Photovoltaic Power Prediction Based on CEEMD-SSA-SVM
摘要
Abstract
In order to solve the problem that the prediction of photovoltaic power is difficult due to random fluctuation,an improved empirical mode decomposition(CEEMD)is proposed to analyze the original photovoltaic power data,and the modal components of different scales are obtained.Then,the sparrow search algorithm(SSA)is introduced to optimize the support vector machine(SVM),and a prediction model of the modal components of different scales is established.Finally,each predicted value is superimposed to obtain the final predicted value of photovoltaic power generation.The simulation results show that the proposed CEEMD-SSA-SVM method can greatly improve the prediction accuracy on the premise that the original photovoltaic power sequence has a small reconstruction error after CEEMD processing.关键词
光伏发电/功率预测/改进的经验模态分解/麻雀搜索算法/支持向量机Key words
photovoltaic power generation/power prediction/improved empirical mode decomposition/sparrow search algorithm/support vector machine分类
信息技术与安全科学引用本文复制引用
魏鹏飞,石新聪,朱咏明,何龙,李杨,巨晓敏,王清彬..基于CEEMD-SSA-SVM的短期光伏发电功率预测[J].水力发电,2024,50(4):87-94,8.基金项目
国家自然科学基金资助项目(52266018) （52266018）
新疆维吾尔自治区重点研发项目(2022B01016-1) （2022B01016-1）
