COMPARISON OF PRINCIPAL COMPONENT ANALYSIS AND ANFIS TO IMPROVE EEVE LABORATORY ENERGY USE PREDICTION PERFORMANCE

Desmira, Desmira and Abu Bakar, Norazhar and Wiryadinata, Romi and Abi Hamid, Mustofa and Kholifah, Nur and Nurtanto, Muhammad (2022) COMPARISON OF PRINCIPAL COMPONENT ANALYSIS AND ANFIS TO IMPROVE EEVE LABORATORY ENERGY USE PREDICTION PERFORMANCE. INDONESIAN JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCE, 27 (2). pp. 970-979. ISSN 2502-4752

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Abstract

The energy use that is in excess of practicum students’ needs and the disturbed comfort that the practicum students experience when conducting practicums in the Electrical eengineering vocational education (EEVE) laboratory. The main objective in this study was to figure out how to predict and streamline the use of electrical energy in the EEVE laboratory. The model used to achieve this research’s goal was called the adaptive neurofuzzy inference system (ANFIS) model, which was coupled with principal component analysis (PCA) feature selection. The use of PCA in data grouping performance aims to improve the performance of the ANFIS model when predicting energy needs in accordance with the standards set by the campus while still taking students’ confidence in conducting practicum activities during campus operating hours into consideration. After some experiments and tests, very good results were obtained in the training: R=1 in training; minimum RMSE=0.011900; epoch of 100 per iteration; and R=0.37522. In conclusion, the ANFIS model coupled with PCA feature selection was excellent at predicting energy needs in the laboratory while the comfort of the students during practicums in the room remained within consideration.

Item Type:	Article
Uncontrolled Keywords:	Adaptive neuro-fuzzy inference system, Efficiency energy, Feature selection, Prediction performance, Principal component analysis
Divisions:	Faculty of Electrical Engineering
Depositing User:	WIZANA ABD JALIL
Date Deposited:	15 Dec 2023 08:35
Last Modified:	15 Dec 2023 08:35
URI:	http://eprints.utem.edu.my/id/eprint/27041
Statistic Details:	View Download Statistic

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