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An improved African vulture optimization algorithm for energy comfort management in occupancy driven smart buildings
Ghulam Fizza1,2, Kushsairy Kadir1, Haidawati Nasir3
1Department of Electrical and Electronic Engineering, Universiti Kuala Lumpur British Malaysian Institute (UniKL BMI), 53100, Selangor, Malaysia.
Scientific Reports
|January 8, 2026
Summary
This study introduces a new algorithm, MTV-AVOA, to optimize smart building comfort and energy use by considering occupancy. It significantly improves indoor environmental quality (IEQ) and reduces energy consumption compared to other methods.
Area of Science:
- Building Science
- Artificial Intelligence
- Sustainable Energy
Background:
- Smart buildings aim to optimize indoor environmental quality (IEQ) and energy efficiency.
- Existing optimization methods often overlook occupancy dynamics, leading to suboptimal comfort and wasted energy.
Purpose of the Study:
- To develop and evaluate a novel optimization algorithm, the Multi-Trial Vector based African Vulture Optimization Algorithm (MTV-AVOA), for smart buildings.
- To jointly optimize temperature, humidity, illumination, and air quality by incorporating occupancy dynamics.
Main Methods:
- Proposed the MTV-AVOA with an occupancy-driven constraint handling mechanism.
- Benchmarked MTV-AVOA against four state-of-the-art algorithms using a 528-hour smart office dataset.
Main Results:
- MTV-AVOA achieved an average comfort index of 0.8026 during occupied hours and 0.750 during non-occupied hours.
- The algorithm demonstrated significant improvement over the non-optimized case (0.665) and reduced energy consumption (occupied: 638.10 kWh, non-occupied: 528.20 kWh).
Conclusions:
- MTV-AVOA effectively balances occupant comfort and energy efficiency by integrating occupancy data into IEQ optimization.
- The proposed algorithm contributes to the development of more sustainable smart buildings.
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