Sustainability, Energy Optimization and Mechanical System Downtime Reduction through Advanced HVAC Operation and Control

References

[1] Aghili, S.A.; Haji Mohammad Rezaei, A.; Tafazzoli, M.; Khanzadi, M.; Rahbar, M. Artificial Intelligence Approaches to Energy Management in HVAC Systems: A Systematic Review. Buildings 2025, 15, 1008.

[2] Peng, Y.; Lei, Y.; Tekler, Z.D.; Antanuri, N.; Lau, S.-K.; Chong, A. Hybrid System Controls of Natural Ventilation and HVAC in Mixed-Mode Buildings: A Comprehensive Review. Energy and Buildings 2022, 276, 112509.

[3] Alanne, K.; Sierla, S. An Overview of Machine Learning Applications for Smart Buildings. Sustainable Cities and Society 2022, 76, 103445.

[4] Sierla, S.; Ihasalo, H.; Vyatkin, V. A Review of Reinforcement Learning Applications to Control of Heating, Ventilation and Air Conditioning Systems. Energies 2022, 15, 3526.

[5] Tien, P.W.; Wei, S.; Darkwa, J.; Wood, C.; Calautit, J.K. Machine Learning and Deep Learning Methods for Enhancing Building Energy Efficiency and Indoor Environmental Quality: A Review. Energy and AI 2022, 10, 100198.

[6] Nelson, W.; Culp, C. Machine Learning Methods for Automated Fault Detection and Diagnostics in Building Systems: A Review. Energies 2022, 15, 5534.

[7] Halhoul Merabet, G.; Essaaidi, M.; Ben Haddou, M.; Qolomany, B.; Qadir, J.; Anan, M.; Al-Fuqaha, A.; Abid, M.R.; Benhaddou, D. Intelligent Building Control Systems for Thermal Comfort and Energy-Efficiency: A Systematic Review of Artificial Intelligence- Assisted Techniques. Renewable and Sustainable Energy Reviews 2021, 144, 110969.

[8] Miao, Y.; Yao, Y.; Hong, X.; Xiong, L.; Zhang, F.; Chen, W. Research on Optimal Control of HVAC System Using Swarm Intelligence Algorithms. Building and Environment 2023, 241, 110467.

[9] Bouabdallaoui, Y.; Lafhaj, Z.; Yim, P.; Ducoulombier, L.; Bennadji, B. Predictive Maintenance in Building Facilities: A Machine Learning-Based Approach. Sensors 2021, 21, 1044.

[10] Xiong, L.; Yao, Y. Study on an Adaptive Thermal Comfort Model with K-Nearest- Neighbors Algorithm. Building and Environment 2021, 202, 108026.

[11] Yu, L.; Xie, W.; Xie, D.; Zou, Y.; Zhang, D.; Sun, Z.; Zhang, L.; Zhang, Y.; Jiang, T. Deep Reinforcement Learning for Smart Home Energy Management. IEEE Internet of Things Journal 2020, 7, 2751-2762.

[12] Yan, K.; Chong, A.; Mo, Y. Generative Adversarial Network for Fault Detection Diagnosis of Chillers. Building and Environment 2020, 172, 106698.

[13] Yu, L.; Sun, Y.; Xu, Z.; Shen, C.; Yue, D.; Jiang, T.; Guan, X. Multi-Agent Deep Reinforcement Learning for HVAC Control in Commercial Buildings. IEEE Transactions on Smart Grid 2021, 12, 407-419.

[14] Brandi, S.; Piscitelli, M.S.; Martellacci, M.; Capozzoli, A. Deep Reinforcement Learning to Optimise Indoor Temperature Control and Heating Energy Consumption in Buildings. Energy and Buildings 2020, 224, 110225.

[15] Du, Y.; Zandi, H.; Kotevska, O.; Kurte, K.; Munk, J.; Amasyali, K.; McKee, E.; Li, F. Intelligent Multi-Zone Residential HVAC Control Strategy Based on Deep Reinforcement Learning. Applied Energy 2021, 281, 116117.

[16] Dai, X.; Liu, J.; Zhang, X. A Review of Studies Applying Machine Learning Models to Predict Occupancy and Window-Opening Behaviours in Smart Buildings. Energy and Buildings 2020, 223, 110159.

[17] Gao, G.; Li, J.; Wen, Y. DeepComfort: Energy-Efficient Thermal Comfort Control in Buildings via Reinforcement Learning. IEEE Internet of Things Journal 2020, 7, 8472-8484.

[18] Zou, Z.; Yu, X.; Ergan, S. Towards Optimal Control of Air Handling Units Using Deep Reinforcement Learning and Recurrent Neural Network. Building and Environment 2020, 168, 106535.

[19] Esrafilian-Najafabadi, M.; Haghighat, F. Occupancy-Based HVAC Control Systems in Buildings: A State-of-the-Art Review. Building and Environment 2021, 197, 107810.

[20] Taheri, S.; Razban, A. Learning-Based CO2 Concentration Prediction: Application to Indoor Air Quality Control Using Demand- Controlled Ventilation. Building and Environment 2021, 205, 108164.

[21] Ahn, K.U.; Park, C.S. Application of Deep Q- Networks for Model-Free Optimal Control Balancing between Different HVAC Systems. Science and Technology for the Built Environment 2020, 26, 61-74.

[22] Deng, Z.; Wang, X.; Jiang, Z.; Zhou, N.; Ge, H.; Dong, B. Evaluation of Deploying Data- Driven Predictive Controls in Buildings on a Large Scale for Greenhouse Gas Emission Reduction. Energy 2023, 270, 126934.

[23] Mohseni, S.-R.; Zeitouni, M.J.; Parvaresh, A.; Abrazeh, S.; Gheisarnejad, M.; Khooban, M.- H. FMI Real-Time Co-Simulation-Based Machine Deep Learning Control of HVAC Systems in Smart Buildings: Digital-Twins Technology. Transactions of the Institute of Measurement and Control 2023, 45, 661-673.

[24] Weinberg, D.; Wang, Q.; Timoudas, T.O.; Fischione, C. A Review of Reinforcement Learning for Controlling Building Energy Systems from a Computer Science Perspective. Sustainable Cities and Society 2023, 89, 104351.

[25] Deng, X.; Zhang, Y.; Qi, H. Towards Optimal HVAC Control in Non-Stationary Building Environments Combining Active Change Detection and Deep Reinforcement Learning. Building and Environment 2022, 211, 108680.

[26] Elnour, M.; Himeur, Y.; Fadli, F.; Mohammedsherif, H.; Meskin, N.; Ahmad, A.M.; Petri, I.; Rezgui, Y.; Hodorog, A. Neural Network-Based Model Predictive Control System for Optimizing Building Automation and Management Systems of Sports Facilities. Applied Energy 2022, 318, 119153.

[27] Liu, X.; Ren, M.; Yang, Z.; Yan, G.; Guo, Y.; Cheng, L.; Wu, C. A Multi-Step Predictive Deep Reinforcement Learning Algorithm for HVAC Control Systems in Smart Buildings. Energy 2022, 259, 124857.

[28] Darwazeh, D.; Gunay, B.; Duquette, J. Development of Inverse Greybox Model- Based Virtual Meters for Air Handling Units. IEEE Transactions on Automation Science and Engineering 2021, 18, 323-336.

[29] Talei, H.; Benhaddou, D.; Gamarra, C.; Benbrahim, H.; Essaaidi, M. Smart Building Energy Inefficiencies Detection through Time Series Analysis and Unsupervised Machine Learning. Energies 2021, 14, 6042.

[30] Jiang, Z.; Risbeck, M.J.; Ramamurti, V.; Murugesan, S.; Amores, J.; Zhang, C.; Lee, Y.M.; Drees, K.H. Building HVAC Control with Reinforcement Learning for Reduction of Energy Cost and Demand Charge. Energy and Buildings 2021, 239, 110833.

@article{1722144,
      author = {AbuBaker Mohammed},
      title = {Sustainability, Energy Optimization and Mechanical System Downtime Reduction through Advanced HVAC Operation and Control},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {10},
      number = {2},
      pages = {436-448},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1722144.pdf},
      abstract = {Heating, ventilation and air-conditioning systems sit at the intersection of building decarbonisation, occupant wellbeing and asset reliability. Yet energy optimisation programmes often treat control efficiency and mechanical downtime as separate problems, even though fouling, sensor drift, valve leakage, unstable sequencing and degraded heat transfer simultaneously increase consumption and failure exposure. This review synthesises research published from 2020 to 2025 on advanced HVAC operation, predictive control, fault detection, digital twins and maintenance analytics. Its aim is to explain how an integrated operating model can minimise energy and carbon intensity while preserving comfort, equipment health and service continuity. A structured evidence review was undertaken across control, energy and building-engineering literature, with studies assessed according to operational scope, data requirements, verification quality and relevance to real facilities. The evidence indicates that forecasting, model-predictive control, reinforcement learning and occupancy-responsive ventilation can reduce avoidable load, but savings are sustained only when control actions are constrained by equipment limits and supported by reliable sensors. Machine-learning fault detection and predictive maintenance improve early recognition of degradation, yet their practical value depends on diagnostic precision, maintenance capacity and post-intervention verification. The paper proposes a unified architecture linking sensing, state estimation, optimisation, supervisory control, fault diagnosis and work management. The central conclusion is that advanced HVAC management should be evaluated as a resilience capability rather than a narrow energy-saving intervention. The strongest programmes jointly govern energy, comfort, emissions, mean time between failures, repair time and unplanned downtime, thereby converting intelligent control into durable operational performance.},
      keywords = {HVAC, Energy Optimisation, Sustainability, Predictive Control, Fault Detection, Predictive Maintenance, Mechanical Downtime, Digital Twin},
      month = {August},
      doi = {https://doi.org/10.64388/IREV10I2-1722144}
  }