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

Azam Zamhuri Fuadi; Irsyad Nashirul Haq; Edi Leksono

doi:10.29207/resti.v5i3.2947

Azam Zamhuri Fuadi Universitas Telkom
Irsyad Nashirul Haq Institut Teknologi Bandung
Edi Leksono Institut Teknologi Bandung

DOI: https://doi.org/10.29207/resti.v5i3.2947

Keywords: Prediction, Electrical Consumption, profile load, machine learning, SUpport vector machine

Abstract

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 optimal parameter values C 1e6 and 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.

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