Heating, Ventilation, and Air Conditioning (HVAC) systems contribute a considerable share of total global energy consumption and carbon dioxide emissions, putting them at the heart of the issues of decarbonization and removing barriers to achieving net-zero emissions and sustainable development goals. Nevertheless, the effective implementation of artificial intelligence (AI)-based methods to optimize energy efficiency while ensuring occupant comfort in multifarious settings remains to be fully realized. This paper provides a systematic review of state-of-the-art practices (2018 and later) using AI algorithms like machine learning (ML), deep learning (DL), and other computation-based techniques that have been deployed to boost HVAC system performance. The review highlights that AI-driven control strategies can reduce energy consumption by up to 40% by dynamically adapting to environmental conditions and occupancy levels. Compared to other work that focuses on single aspects of HVAC management, this work deals with the methods of control and maintenance in a comprehensive manner. Rather than focusing on abstract applications of machine learning models, this study underlines their applicability in HVAC systems, bridging the science–practice gap. This study highlights the prospective role AI could play, on the one hand, by enhancing HVAC systems’ incorporation, energy consumption, and building technologies, while, on the other hand, also addressing the potential uses AI can have in practical applications in the future, bridging gaps and addressing challenges.

Purpose: Hospital heating, ventilation and air conditioning (HVAC) systems are essential to patient safety and wellness. System malfunctions, however, may result in energy waste and even pose health dangers. This project aims to provide a fault detection and diagnostics framework designed primarily for HVAC systems in hospitals. Design/methodology/approach: In order to identify problems in hospital air handling units, the study uses a data-driven methodology that makes use of Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) models. To address the problem of uneven data, the dataset is balanced. Other machine learning classifiers, such as Logistic Regression, Multilayer Perceptron, Support Vector Machine, Random Forest, Gradient Boosting and eXtreme Gradient Boosting, are compared to see how well the LSTM and GRU models perform. Findings: Regarding defect detection, the LSTM and GRU models outperform traditional classifiers in terms of both accuracy and computation speed, with high accuracy rates surpassing 90%. Due to its simpler design, GRU achieves higher accuracy and performs faster calculations than LSTM. These recurrent models work well to identify temporal relationships in time-series data, which is crucial for detecting HVAC system problems. Originality/value: This study closes a research gap by concentrating on issue identification in hospital HVAC systems using actual data. It illustrates how deep learning may increase the precision of fault identification and computational efficiency in medical settings by utilizing LSTM and GRU models.

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