基于相似日小波支持向量机的短期电力负荷预测

Short Term Load Forecasting Using Wavelet Transform and SVM Based on Similar-Days

对受不确定性影响的短期电力负荷,本文给出一种基于小波支持向量机的预测方法。采用小波变换将日负荷数据分解到不同尺度上,利用各相似日低频部分的最大最小负荷构造相似系数,通过支持向量机预测一天中最大和最小负荷,结合相似系数得到预测日低频部分各时刻的预测值;对于高频部分采用各时刻均方加权的方法预测负荷值,把各部分的负荷值叠加得到完整的负荷预测值。用山东某电力公司的数据进行数据仿真,取得了较好的预测效果。

For the short-term electric power load with uncertain influence factors, a novel approach is proposed by combining the wavelet transform （WT） and support vector machine （SVM）. From the signal analysis point of view, load can also be considered as a linear combination of different frequencies. By the WT, the different load sequence components are projects to the different scales. Based on the maximum and minimum loads of the approximate part, the similar coefficients are given. The support vector machine is used to forecast the maximum and minimum load of the forecasting day. The short term load forecasting is forecasted by summing the predicted approximate part and the weighted detail parts. The numerical simulation results show this approach is encouraging.