Predictive Maintenance of HVAC Systems using Deep Learning for Optimized Building Energy Efficiency
Buildings consume approximately one-third of the world's energy, with the commercial and housing sectors' Heating, Ventilation, and Air Conditioning (HVAC) systems being the largest contributors to energy. Energy wastage is significant as a result of system faults, which indicates the impo...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
K. N. Toosi University of Technology
2025-05-01
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| Series: | Numerical Methods in Civil Engineering |
| Subjects: | |
| Online Access: | https://nmce.kntu.ac.ir/article_223666_9a34b3f92632f6be919cb441284e8c52.pdf |
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| Summary: | Buildings consume approximately one-third of the world's energy, with the commercial and housing sectors' Heating, Ventilation, and Air Conditioning (HVAC) systems being the largest contributors to energy. Energy wastage is significant as a result of system faults, which indicates the importance of efficient control of energy in HVAC in saving energy as well as providing comfort to the occupants. Techniques in Artificial Intelligence (AI), such as Machine Learning (ML) and Deep Learning (DL), are now used to optimize HVAC energy efficiency as well as facilitate predictive maintenance, which reduces downtime as well as costs. Past research has underestimated qualitative faults analysis in HVAC systems or suffered from inaccurate identification using AI. This paper proposes an innovative AI-based framework to manage energy in buildings. The framework uses Fault Tree Analysis (FTA) initially to perform qualitative analysis regarding the effect of HVAC system faults in energy consumption. Next, it applies AI models, namely Long Short-Term Memory (LSTM) networks as well as Gated Recurrent Unit (GRU) networks, trained using experimental data from real-building environments. The models are designed to detect faults accurately as well as in time. The main goal is to save energy from wastage as well as ensure occupant comfort through timely maintenance as well as replacement of faulty equipment. Most notably, the GRU approach showed higher accuracy in the identification of faults compared to LSTM. The framework's accurate identification of the occurrence as well as the nature of the faults is an improvement in the efficiency of the building. |
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| ISSN: | 2345-4296 2783-3941 |