基于经验模态分解与特征相关分析的短期负荷预测方法

提出了一种基于经验模态分解与特征相关分析的短期负荷预测新方法。该方法从分解负荷序列入手,采用经验模态分解将原始负荷时间序列分解成不同频率的本征模函数(IMF)分量和残差分量,以弱化复杂影响因素环境下原始序列的波动性,获取更具规律性的分量。然后运用最小冗余度最大相关性标准(mRMR)技术分析各IMF分量和日类型、天气、电价等特征信息之间的相关性,获得最佳特征集。最后采用基于智能算法的最小二乘支持向量机(LSSVM)负荷预测模型对各经验模态分量进行预测,并将各分量预测结果叠加得到最终负荷预测值。以某电网实际数据进行算例分析,结果表明所提出的组合模型能够更准确地对外部因素敏感的短期负荷进行预测。

Short-term Load Forecasting Method Based on Empirical Mode Decomposition and Feature Correlation Analysis

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.