Indoor Comfort and Energy Consumption Optimization Using an Inertia Weight Artificial Bee Colony Algorithm

A comfortable indoor environment contributes to a better quality of life and wellbeing for its occupants. The indoor temperature, lighting, and air quality are the main controlling factors of user comfort levels. The optimum control of the lighting, air conditioners, and air ventilators helps in max...

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Vydané v:Algorithms Ročník 15; číslo 11; s. 395
Hlavní autori: Baharudin, Farah Nur Arina, Ab. Aziz, Nor Azlina, Abdul Malek, Mohamad Razwan, Ghazali, Anith Khairunnisa, Ibrahim, Zuwairie
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.10.2022
Predmet:
Air conditioners
Air conditioning
Air quality
Air quality management
Algorithms
artificial bee colony
Comfort
comfort index
Coronaviruses
COVID-19
Disease transmission
Energy consumption
Energy use
Equipment and supplies
Food
Genetic algorithms
Indoor air pollution
Indoor air quality
Inertia
inertia weight
Lighting
Maximization
Mental health
Optimization
Optimization algorithms
Pandemics
residential building
Search algorithms
Swarm intelligence
ISSN:1999-4893, 1999-4893
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Shrnutí:A comfortable indoor environment contributes to a better quality of life and wellbeing for its occupants. The indoor temperature, lighting, and air quality are the main controlling factors of user comfort levels. The optimum control of the lighting, air conditioners, and air ventilators helps in maximizing the user’s comfort level. Nonetheless, the energy consumption of these appliances needs to be taken into consideration to minimize the operational cost and at the same time provide an environmentally friendly system. Comfort level maximization and energy consumption minimization are optimization problems. This issue is becoming more important due to the lifestyle changes caused by the COVID-19 pandemic that resulted in more time spent at home and indoors. Inertia weight artificial bee colony (IW-ABC) algorithms using linearly increasing, linearly decreasing, and exponentially increasing inertia are proposed here for the optimization of the indoor comfort index and energy usage. The multi-objective problem is tackled as a weighted single objective optimization problem. The proposed solution is tested using a dataset of 48 environmental conditions. The results of the simulation show that the IW-ABC performs better than the original ABC and other benchmark algorithms and the IW-ABC with linear increasing inertia weight has the most improved convergence behavior.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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content type line 14
ISSN:1999-4893
1999-4893
DOI:10.3390/a15110395