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Laboratory prediction energy control system based on artificial intelligence network

The use of electrical energy increases globally every year. The laboratory prediction energy control system (LPECS) predicted energy demand. This research was conducted in the Electrical Engineering Vocational Education laboratory by comparing the artificial neural fuzzy system (ANFIS) with the fuzz...

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Main Authors: Abu Bakar, Norazhar, Desmira, Abi Hamid, Mustofa, Hashim, Mohd Ruzaini, Wiryadinata, Romi
Format: Article
Language:English
Published: Institute of Advanced Engineering and Science (IAES) 2022
Online Access:http://eprints.utem.edu.my/id/eprint/26480/2/1821-9571-1-PB.PDF
http://eprints.utem.edu.my/id/eprint/26480/
https://beei.org/index.php/EEI/article/view/1821/2722
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spelling my.utem.eprints.264802023-06-12T12:52:01Z http://eprints.utem.edu.my/id/eprint/26480/ Laboratory prediction energy control system based on artificial intelligence network Abu Bakar, Norazhar Desmira Abi Hamid, Mustofa Hashim, Mohd Ruzaini Wiryadinata, Romi The use of electrical energy increases globally every year. The laboratory prediction energy control system (LPECS) predicted energy demand. This research was conducted in the Electrical Engineering Vocational Education laboratory by comparing the artificial neural fuzzy system (ANFIS) with the fuzzy logic. The comparison of methods aimed to determine their reliability in the energy demand prediction systems. The results showed that the minimum value of the target data using the conventional method (actual data) was 44.42%. Meanwhile, the prediction data using the ANFIS method was 44.33%, and the prediction data using the fuzzy method was 59.31%. The maximum value of the conventional ways (actual data) of ANFIS and fuzzy was similar by 77.59%. The RMSE ANFIS value was 0.1355%, the mean absolute percentage error (MAPE) was 0.2791%, and the fuzzy logic was 0.1986%. Thus, the ANFIS is applicable to determine the minimum and maximum values. Meanwhile, fuzzy can only show the maximum value but cannot reach the minimum value properly. Institute of Advanced Engineering and Science (IAES) 2022-06 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/26480/2/1821-9571-1-PB.PDF Abu Bakar, Norazhar and Desmira and Abi Hamid, Mustofa and Hashim, Mohd Ruzaini and Wiryadinata, Romi (2022) Laboratory prediction energy control system based on artificial intelligence network. Bulletin of Electrical Engineering and Informatics, 11 (3). pp. 1280-1288. ISSN 2302-9285 https://beei.org/index.php/EEI/article/view/1821/2722 10.11591/eei.v11i3.1821
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description The use of electrical energy increases globally every year. The laboratory prediction energy control system (LPECS) predicted energy demand. This research was conducted in the Electrical Engineering Vocational Education laboratory by comparing the artificial neural fuzzy system (ANFIS) with the fuzzy logic. The comparison of methods aimed to determine their reliability in the energy demand prediction systems. The results showed that the minimum value of the target data using the conventional method (actual data) was 44.42%. Meanwhile, the prediction data using the ANFIS method was 44.33%, and the prediction data using the fuzzy method was 59.31%. The maximum value of the conventional ways (actual data) of ANFIS and fuzzy was similar by 77.59%. The RMSE ANFIS value was 0.1355%, the mean absolute percentage error (MAPE) was 0.2791%, and the fuzzy logic was 0.1986%. Thus, the ANFIS is applicable to determine the minimum and maximum values. Meanwhile, fuzzy can only show the maximum value but cannot reach the minimum value properly.
format Article
author Abu Bakar, Norazhar
Desmira
Abi Hamid, Mustofa
Hashim, Mohd Ruzaini
Wiryadinata, Romi
spellingShingle Abu Bakar, Norazhar
Desmira
Abi Hamid, Mustofa
Hashim, Mohd Ruzaini
Wiryadinata, Romi
Laboratory prediction energy control system based on artificial intelligence network
author_facet Abu Bakar, Norazhar
Desmira
Abi Hamid, Mustofa
Hashim, Mohd Ruzaini
Wiryadinata, Romi
author_sort Abu Bakar, Norazhar
title Laboratory prediction energy control system based on artificial intelligence network
title_short Laboratory prediction energy control system based on artificial intelligence network
title_full Laboratory prediction energy control system based on artificial intelligence network
title_fullStr Laboratory prediction energy control system based on artificial intelligence network
title_full_unstemmed Laboratory prediction energy control system based on artificial intelligence network
title_sort laboratory prediction energy control system based on artificial intelligence network
publisher Institute of Advanced Engineering and Science (IAES)
publishDate 2022
url http://eprints.utem.edu.my/id/eprint/26480/2/1821-9571-1-PB.PDF
http://eprints.utem.edu.my/id/eprint/26480/
https://beei.org/index.php/EEI/article/view/1821/2722
_version_ 1769847404333367296
score 13.211869

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