Comparative Analysis of Optimization Algorithms for Energy Consumption Minimization in HVAC Systems
Authors: 
Shruti Maheshwari, Dhanraj Shivendrakumar Dubey, Vikas Gulia, Nitin SolkeAuthors Info & Claims
Shruti Maheshwari, Dhanraj Shivendrakumar Dubey, Vikas Gulia, Nitin SolkeAuthors Info & ClaimsArticle No.: 168, Pages 1 - 10
Abstract
Energy optimization is essential in the pursuit of a more sustainable future. In this research paper we describe implementation and comparison of optimization algorithms for reducing energy consumption in HVAC (Heating, Ventilation and Air Conditioning) Systems. We explore two categories of algorithms: swarm-based and physics-based. The swarm-based algorithms involve Particle Swarm Optimization, Ant Colony Optimization, Firefly Algorithm, and Cuckoo Search algorithm whereas the physics-based algorithms consist of Simulated Annealing and Gravitational Search Algorithms. We apply these algorithms to optimize damper settings of an HVAC system based on real-life energy consumption data for reducing energy consumptions. Comparing the performances of these diverse algorithms, our study shows that their energy consumption is lowered by almost half. Firefly Algorithm resulted in better energy optimization when compared to other algorithms. The energy before optimization is around 508003 kWh and after optimization the energy consumption is reduced to a range of 20000-25500 kWh. Our research covers methodology used, experiences gained, and results presented as well as what those mean in context. Findings from this research reveal how optimization algorithms are capable of promoting sustainable energy practices.
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November 2023
1215 pages
ISBN:9798400709418
DOI:10.1145/3647444
Copyright © 2023 ACM.
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Published: 13 May 2024
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ICIMMI 2023
ICIMMI 2023: International Conference on Information Management & Machine Intelligence
November 23 - 25, 2023
Jaipur, India
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