Daylight-adaptive lighting control techniques: a comparative analysis of particle swarm optimization and firefly algorithm
Lighting in commercial buildings consumes a substantial amount of energy. Therefore, this paper developed particle swarm optimization (PSO) and firefly algorithm (FA) as control techniques for lighting systems to improve energy efficiency and satisfy occupantsꞌ visual comfort in an indoor envir...
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my.uthm.eprints.112242024-07-14T02:43:15Z http://eprints.uthm.edu.my/11224/ Daylight-adaptive lighting control techniques: a comparative analysis of particle swarm optimization and firefly algorithm Wagiman, Khairul Rijal Md Adnan, Mohd Faiz Hussin, Imran Md Nor, Mohd Hairwan Din, Asri QA299.6-433 Analysis Lighting in commercial buildings consumes a substantial amount of energy. Therefore, this paper developed particle swarm optimization (PSO) and firefly algorithm (FA) as control techniques for lighting systems to improve energy efficiency and satisfy occupantsꞌ visual comfort in an indoor environment. An office room was considered to test the performance of the PSO and FA techniques. The proposed methods showed superior performance in minimizing the cost of energy consumption by more than 60% while satisfying illuminance-based metrics mentioned by the European Standard EN 12464-1. Based on the comparative result, PSO outperformed FA by 3% in energy savings. Due to its performance, the proposed PSO method can be utilized for other types of buildings 2024-03-04 Conference or Workshop Item PeerReviewed text en http://eprints.uthm.edu.my/11224/1/P16717_671d16e8b028d3ace0991ad1a4cb5720%208.pdf Wagiman, Khairul Rijal and Md Adnan, Mohd Faiz and Hussin, Imran and Md Nor, Mohd Hairwan and Din, Asri (2024) Daylight-adaptive lighting control techniques: a comparative analysis of particle swarm optimization and firefly algorithm. In: IEEE 4th International Conference in Power Engineering Applications (ICPEA). http:\\10.1109/ICPEA60617.2024.10498533 |
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QA299.6-433 Analysis Wagiman, Khairul Rijal Md Adnan, Mohd Faiz Hussin, Imran Md Nor, Mohd Hairwan Din, Asri Daylight-adaptive lighting control techniques: a comparative analysis of particle swarm optimization and firefly algorithm |
description |
Lighting in commercial buildings consumes a
substantial amount of energy. Therefore, this paper developed
particle swarm optimization (PSO) and firefly algorithm (FA)
as control techniques for lighting systems to improve energy
efficiency and satisfy occupantsꞌ visual comfort in an indoor
environment. An office room was considered to test the
performance of the PSO and FA techniques. The proposed
methods showed superior performance in minimizing the cost
of energy consumption by more than 60% while satisfying
illuminance-based metrics mentioned by the European
Standard EN 12464-1. Based on the comparative result, PSO
outperformed FA by 3% in energy savings. Due to its
performance, the proposed PSO method can be utilized for
other types of buildings |
format |
Conference or Workshop Item |
author |
Wagiman, Khairul Rijal Md Adnan, Mohd Faiz Hussin, Imran Md Nor, Mohd Hairwan Din, Asri |
author_facet |
Wagiman, Khairul Rijal Md Adnan, Mohd Faiz Hussin, Imran Md Nor, Mohd Hairwan Din, Asri |
author_sort |
Wagiman, Khairul Rijal |
title |
Daylight-adaptive lighting control techniques: a comparative analysis of particle swarm optimization and firefly algorithm |
title_short |
Daylight-adaptive lighting control techniques: a comparative analysis of particle swarm optimization and firefly algorithm |
title_full |
Daylight-adaptive lighting control techniques: a comparative analysis of particle swarm optimization and firefly algorithm |
title_fullStr |
Daylight-adaptive lighting control techniques: a comparative analysis of particle swarm optimization and firefly algorithm |
title_full_unstemmed |
Daylight-adaptive lighting control techniques: a comparative analysis of particle swarm optimization and firefly algorithm |
title_sort |
daylight-adaptive lighting control techniques: a comparative analysis of particle swarm optimization and firefly algorithm |
publishDate |
2024 |
url |
http://eprints.uthm.edu.my/11224/1/P16717_671d16e8b028d3ace0991ad1a4cb5720%208.pdf http://eprints.uthm.edu.my/11224/ http:\\10.1109/ICPEA60617.2024.10498533 |
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