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

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

Machine Learning, Prediction of Electricity Consumption, Profile Load, Support Vector Machine

Predicted electricity consumption is needed to perform energy management. Electricity consumption prediction is also very important in the development of intelligent power grids and advanced electrification network information. we implement a Support Vector Machine (SVM) to predict electrical loads and results compared to measurable electrical loads. Laboratory electrical loads have their own characteristics when compared to residential, commercial, or industrial, we use electrical load data in energy management laboratories to be used to be predicted. C and Gamma as searchable parameters use GridSearchCV to get optimal SVM input parameters. Our prediction data is compared to measurement data and is searched for accuracy based on RMSE (Root Square Mean Error), MAE (Mean Absolute Error) and MSE (Mean Squared Error) values. Based on this we get the optimalparameter values C 1e6and Gamma 2.97e-07, with the result RSME (Root Square Mean Error) ; 0.37, MAE (meaning absolute error); 0.21 and MSE (Mean Squared Error); 0.14

zam Zamhuri Fuadi1, Irsyad Nashirul Haq2, Edi Leksono3

https://jurnal.iaii.or.id/index.php/RESTI/issue/view/23

Telkom University

20 juni 2021

Fajar bagus W

PDF

Indonesia

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