A prediction model for electric load based on an immune support vector machine(SVM)algo-rithm is proposed to address the issues of high randomness and poor stability in the electric load of resi-dential areas.Considering various factors that affect the electric load of residents,the historical electric consumption and relevant climate data of residential areas are used as the processing objects.The princi-pal component analysis(PCA)algorithm is utilized to preprocess the historical data of the power grid,and the immune algorithm is combined to preprocess the data by forming data clusters and defining labels for training the prediction model.To improve the accuracy of the model,the biological immune optimiza-tion algorithm is used to optimize the parameters of the SVM model.In the load prediction process,the prediction error is used as the basis for feedback tuning of the prediction model.The prediction perform-ance of the immune SVM algorithm load prediction model is compared with that of the commonly used back propagation(BP)neural network and SVM algorithm model.The short-term and medium-term prediction accuracies of the immune SVM algorithm load prediction model are both above 98％,demon-strating good accuracy and robustness.
关键词
支持向量机(SVM)/PCA/免疫算法/负荷预测
Key words
support vector machine(SVM)/PCA/immune algorithm/load forecasting
维普
万方数据