Using the evolutionary mating algorithm for optimizing the user comfort and energy consumption in smart building
This paper presents a simulation study focused on optimizing user comfort and energy consumption in smart buildings. Managing energy efficiently in smart buildings poses a significant challenge. The aim of this research is to achieve a high level of occupant comfort while minimizing energy usage. Th...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English English |
| Published: |
Elsevier Ltd
2023
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/40751/1/Using%20the%20evolutionary%20mating%20algorithm%20for%20optimizing%20the%20user%20comfort%20and%20energy%20consumption.pdf http://umpir.ump.edu.my/id/eprint/40751/2/Using%20the%20evolutionary%20mating%20algorithm%20for%20optimizing%20the%20user%20comfort%20and%20energy%20consumption%20in%20smart%20building.pdf http://umpir.ump.edu.my/id/eprint/40751/ https://doi.org/10.1016/j.jobe.2023.107139 |
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| Summary: | This paper presents a simulation study focused on optimizing user comfort and energy consumption in smart buildings. Managing energy efficiently in smart buildings poses a significant challenge. The aim of this research is to achieve a high level of occupant comfort while minimizing energy usage. The study considers three fundamental parameters for measuring user comfort: thermal comfort, visual comfort, and indoor air quality (IAQ). Data from temperature, illumination, and CO2 sensors are collected to assess the indoor environment. Based on this information, smart building systems can dynamically adjust heating, cooling, lighting, and ventilation to optimize energy usage and ensure occupant comfort. To address the optimization problem, the Evolutionary Mating Algorithm (EMA) is proposed. EMA belongs to the evolutionary computation group of nature-inspired metaheuristic algorithms and offers a promising solution. A comparative analysis is conducted with other well-known algorithms such as Particle Swarm Optimization (PSO), Differential Evolution (DE), Ant Colony Optimization (ACO), Biogeography-Based Optimization (BBO), Teaching-Learning Based Optimization (TLBO), and Beluga Whale Optimization (BWO). The findings demonstrate the effectiveness of EMA in achieving optimum comfort with minimal energy consumption in smart building systems. |
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