Support Vector Machine to Predict Electricity Consumption in the Energy Management Laboratory

Predicted electricity consumption is needed to perform energy management. Electricity prediction also very important in the development of intelligent power grids and advanced electrification network information. we implement a Support Vector Machine (SVM) predict electrical loads results compared measurable loads. Laboratory have their own characteristics when residential, commercial, or industrial, use load data management laboratories be used predicted. C Gamma as searchable parameters GridSearchCV get optimal SVM input parameters. Our measurement searched for accuracy based on RMSE (Root Square Mean Error), MAE (Mean Absolute Error) MSE Squared values. Based this parameter values 1e6 2.97e-07, with result RSME ; 0.37, (meaning absolute error); 0.21 Error); 0.14.