电力系统自动化2019，Vol.43Issue(5)：46-52,7.DOI:10.7500/AEPS20180404008
基于经验模态分解与特征相关分析的短期负荷预测方法
Short-term Load Forecasting Method Based on Empirical Mode Decomposition and Feature Correlation Analysis
摘要
Abstract
A new short-term load forecasting method based on empirical mode decomposition (EMD) and feature correlation analysis is proposed. The method begins with the decomposition load sequence and uses the EMD to decompose the original load time series into different frequency intrinsic mode function (IMF) components and residual components to weaken the volatility of the original sequence under the environment of complex influence factors and obtain more regular components. Then, the minimal redundancy maximal relevance (mRMR) criterion is used to analyze the correlation between each IMF component and feature information (such as the type of day, weather and electricity price) to obtain the best feature set. Finally, the least squares support vector machine (LSSVM) load forecasting model based on intelligent algorithm is adopted to predict each component and superpose each component prediction result to get the final load forecasting. Taking actual data of a power grid as an example, the results show that the proposed composition model can predict the short-term load which is sensitive to external factors more accurately.关键词
负荷预测/经验模态分解/智能算法/最小冗余度最大相关性Key words
load forecasting/empirical mode decomposition/intelligent algorithm/minimal redundancy maximal relevance criterion引用本文复制引用
孔祥玉,李闯,郑锋,于力,马溪原..基于经验模态分解与特征相关分析的短期负荷预测方法[J].电力系统自动化,2019,43(5):46-52,7.基金项目
国家重点研发计划资助项目(2017YFB0902902) （2017YFB0902902）
国家自然科学基金资助项目(51377119) （51377119）
This work is supported by National Key R&O Program of China (No. 2017YFB0902902) and National Natural Science Foundation of China (NO.51377119). （No. 2017YFB0902902）
