电力系统自动化2016，Vol.40Issue(15)：67-72,92,7.DOI:10.7500/AEPS20160229012
实现影响因素多源异构融合的短期负荷预测支持向量机算法
Short-term Load Forecasting Support Vector Machine Algorithm Based on Multi-source Heterogeneous Fusion of Load Factors
摘要
Abstract
A method to select optimal multiple kernels developed from multiple kernel function is proposed for short ‐term load forecasting in the big data environment of smart grid , multi‐source heterogeneous load factors taken into account . The multiple kernel function is able to describe the distribution characteristics of the factors , cope with their variations and improve the accuracy of load forecasting . Load factors such as historical load , air temperature , air pressure , relative humidity , rainfall , wind direction , wind speed , holidays and electricity price are selected as multi‐source heterogeneous factors . Three methods ( the sample distribution method , single variable method and rank space diversity method) are adopted to establish optimal multiple kernels , and parallel multiple kernel support vector machine ( SVM ) load forecasting algorithm is based on double layer multi kernel learning algorithm . A Hadoop cluster is built for conducting experiments of short‐term load forecasting . Experimental results show that the average relative error of multiple kernel SVM is smaller than single kernel SVM s , and the accuracy of multiple kernel SVM model based on double layer multiple kernel learning algorithm and norm is the highest . Therefore , multiple kernel SVM can tackle the multi‐source heterogeneous data in the load forecasting effectively , and the speed and accuracy of load forecasting can be improved by parallel processing .关键词
大数据/多源异构特性/支持向量机(SVM)/负荷预测/并行化Key words
big data/multi-source heterogeneous characteristics/support vector machine (SVM)/load forecasting/paralleling引用本文复制引用
吴倩红,高军,侯广松,韩蓓,汪可友,李国杰..实现影响因素多源异构融合的短期负荷预测支持向量机算法[J].电力系统自动化,2016,40(15):67-72,92,7.基金项目
国家自然科学基金资助项目（51407116）；国家科技支撑计划资助项目（2015BAA01B02）。 （）
