Open AccessSupport Vector Machine to Predict Electricity Consumption in the Energy Management Laboratory
Author(s) -
Azam Zamhuri Fuadi,
Irsyad Nashirul Haq,
Edi Leksono
Publication year - 2021
Publication title -
jurnal resti (rekayasa sistem dan teknologi informasi)
Language(s) - English
Resource type - Journals
ISSN - 2580-0760
DOI - 10.29207/resti.v5i3.2947
Subject(s) - mean squared error , mean absolute percentage error , support vector machine , approximation error , root mean square , electricity , mean absolute error , energy consumption , electrical load , electric power , computer science , statistics , simulation , mathematics , power (physics) , artificial intelligence , engineering , electrical engineering , voltage , physics , quantum mechanics
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.