基于小波变换和SVM算法的微电网短期负荷预测研究  

Short-term Load Forecasting of Microgrid Based on Wavelet Transform and Support Vector Machines

作　　者：杨再鹤[1] 向铁元[1] 郑丹[1] 

机构地区：[1]武汉大学电气工程学院,湖北武汉430072

出　　处：《现代电力》2014年第3期74-79,共6页Modern Electric Power

摘　　要：为了满足微电网的建设和发展对其负荷预测的精度和方法适应性提出的更高要求,本文在时域和频域上比较分析了微电网负荷曲线和传统负荷曲线,得出了微电网负荷波动性更强的结论,然后根据微电网负荷的特点,考虑微电网负荷受星期类型和气象因素的影响,提出对微电网负荷进行离散小波分解的基础上,建立支持向量机(SVM)模型对各层分量分别进行预测,最后运用分解关系得出预测结果。研究表明,与直接应用SVM模型预测相比,分解微电网负荷曲线后再进行SVM模型预测能够实现更高的预测精度,更适用于当前微电网短期负荷预测需要。To meet the higher requirement of the load fore casting accuracy and method adaptability introduced by the construction and development of microgrid, the load curves of a typical microgrid are compared and analyzed in time and frequency domains, and stronger conclusion of load fluctuation of microgrid is obtained. Then, according to the load characteristics of microgrid, the influence of week types and meteorological factors on microgrid load is consid ered. Furthermore, the Discrete Wavelet Transform （DWT） is used to analyze microgrid load, and layer compo nent is forecasted by support vector machines （SVM） mod el. In the end, the forecasting results are drawn by the ap plication of decomposition formula. Research results show that the load forecast by SVM model after decomposing mi crogrid load curves has higher prediction accuracy by compa ring with that by single SVM algorithm, which is more suit able for shortterm load forecasting of microgrid.

关 键 词：微电网 短期负荷预测 离散小波变换 支持向量机 波动性 

分 类 号：TM715[电气工程—电力系统及自动化]