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Energy Management Model for HVAC Control Supported by Reinforcement Learning

Author

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  • Pedro Macieira

    (GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto (P.PORTO), P-4200-072 Porto, Portugal)

  • Luis Gomes

    (GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto (P.PORTO), P-4200-072 Porto, Portugal)

  • Zita Vale

    (GECAD—Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Polytechnic of Porto (P.PORTO), P-4200-072 Porto, Portugal)

Abstract

Heating, ventilating, and air conditioning (HVAC) units account for a significant consumption share in buildings, namely office buildings. Therefore, this paper addresses the possibility of having an intelligent and more cost-effective solution for the management of HVAC units in office buildings. The method applied in this paper divides the addressed problem into three steps: (i) the continuous acquisition of data provided by an open-source building energy management systems, (ii) the proposed learning and predictive model able to predict if users will be working in a given location, and (iii) the proposed decision model to manage the HVAC units according to the prediction of users, current environmental context, and current energy prices. The results show that the proposed predictive model was able to achieve a 93.8% accuracy and that the proposed decision tree enabled the maintenance of users’ comfort. The results demonstrate that the proposed solution is able to run in real-time in a real office building, making it a possible solution for smart buildings.

Suggested Citation

  • Pedro Macieira & Luis Gomes & Zita Vale, 2021. "Energy Management Model for HVAC Control Supported by Reinforcement Learning," Energies, MDPI, vol. 14(24), pages 1-14, December.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:24:p:8210-:d:697081
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    References listed on IDEAS

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    1. Dana-Mihaela Petroșanu & George Căruțașu & Nicoleta Luminița Căruțașu & Alexandru Pîrjan, 2019. "A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal B," Energies, MDPI, vol. 12(24), pages 1-64, December.
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    4. Robert Lou & Kevin P. Hallinan & Kefan Huang & Timothy Reissman, 2020. "Smart Wifi Thermostat-Enabled Thermal Comfort Control in Residences," Sustainability, MDPI, vol. 12(5), pages 1-15, March.
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    Citations

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    Cited by:

    1. Panagiotis Michailidis & Iakovos Michailidis & Dimitrios Vamvakas & Elias Kosmatopoulos, 2023. "Model-Free HVAC Control in Buildings: A Review," Energies, MDPI, vol. 16(20), pages 1-45, October.
    2. Omar al-Ani & Sanjoy Das & Hongyu Wu, 2023. "Imitation Learning with Deep Attentive Tabular Neural Networks for Environmental Prediction and Control in Smart Home," Energies, MDPI, vol. 16(13), pages 1-19, June.
    3. Guillermo Bejarano & João M. Lemos & Javier Rico-Azagra & Francisco R. Rubio & Manuel G. Ortega, 2022. "Energy Management of Refrigeration Systems with Thermal Energy Storage Based on Non-Linear Model Predictive Control," Mathematics, MDPI, vol. 10(17), pages 1-27, September.
    4. Prasertsak Charoen & Nathavuth Kitbutrawat & Jasada Kudtongngam, 2022. "A Demand Response Implementation with Building Energy Management System," Energies, MDPI, vol. 15(3), pages 1-21, February.
    5. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
    6. Dongsu Kim & Jongman Lee & Sunglok Do & Pedro J. Mago & Kwang Ho Lee & Heejin Cho, 2022. "Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends," Energies, MDPI, vol. 15(19), pages 1-30, October.

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