基于EMD-Bayes-SVR组合模型的短期电力负荷预测  

Short-Term Load Forecasting Based on EMD-Bayes-SVR Combined Model

作　　者：王雨前 王万雄[1] WANG Yuqian;WANG Wanxiong(College of Sciences,Gansu Agricultural University,Lanzhou 730070,China)

机构地区：[1]甘肃农业大学理学院,甘肃兰州730070

出　　处：《电子科技》2023年第12期64-71,共8页Electronic Science and Technology

基　　金：国家自然科学基金(11971214)。

摘　　要：短期电力负荷是电力供需平衡的关键,针对短期电力负荷预测精度问题,文中提出了EMD(Empirical Mode Decomposition)-Bayes-SVR(Support Vector Regression)组合预测模型,即将原始电力负荷序列通过EMD方法分解为若干个IMF(Intrinsic Mode Function)和1个Res(Residual),依据Hurst指数将各IMF重构为高频分量、低频分量和残差分量,利用贝叶斯优化算法对SVR进行参数寻优,将寻优得到的最佳参数带入SVR并对重构后的3个分量分别进行预测,将3个分量的预测值相加得到最终预测结果。以美国内布拉斯加州的历史电力负荷数据为例,建立8种单一预测模型和7种组合预测模型作为参照模型,对该地的电力负荷序列进行预测。实验结果表明,EMD-Bayes-SVR组合预测模型能够有效预测短期电力负荷的变化趋势,其MAE(Mean Absolute Error)、RMSE(Root Mean Square Error)和MAPE(Mean Absolute Percentage Error)这3种误差评价指标数值相对于SVR模型分别降低了29.84%、32.05%和22%,并显著低于其它参照模型。Short-term power load is the key to the balance of power supply and demand,in view of the short-term power load prediction accuracy problem,the EMD(Empirical Mode Decomposition)-Bayes-SVR(Support Vector Regression)combination prediction model is proposed,that is the original power load sequence is decomposed into several IMF(Intrinsic Mode Function)and a Res(Residual)by EMD method,and each IMF is reconstructed into high frequency components,low frequency components and residual components according to the Hurst index,and the parameters optimization of SVR are optimized by Bayesian optimization algorithm.The optimal parameters obtained by the optimization are brought into the SVR and the reconstructed three components are predicted separately,and the predicted values of the three components are added together to obtain the final prediction result.Taking the historical power load data of Nebraska in the United States as an example,eight single prediction models and seven combined prediction models are established as reference models to predict the power load series in this area.Experimental results show that the combined EMD-Bayes-SVR prediction model can effectively predict the change trend of short-term power load,and the error evaluation indexes of MAE(Mean Absolute Error),RMSE(Root Mean Square Error)and MAPE(Mean Absolute Percentage Error)are decreased by 29.84%,32.05%and 22%,respectively when compared with the SVR model,which are significantly lower than other reference models.

关 键 词：短期电力负荷预测 预测精度 经验模态分解 HURST指数 支持向量回归机 贝叶斯优化算法 组合预测模型 误差评价 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]