COMPARISON OF PRINCIPAL COMPONENT ANALYSIS AND ANFIS TO IMPROVE EEVE LABORATORY ENERGY USE PREDICTION PERFORMANCE
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 s...
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Main Authors: | , , , , , |
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Format: | Article |
Language: | English |
Published: |
Institute of Advanced Engineering and Science
2022
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Online Access: | http://eprints.utem.edu.my/id/eprint/27041/2/0181919092023360.PDF http://eprints.utem.edu.my/id/eprint/27041/ https://ijeecs.iaescore.com/index.php/IJEECS/article/view/27111 |
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Summary: | 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. |
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