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Generalizable occupant-driven optimization model for domestic hot water production in NZEB

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  • Kazmi, H.
  • D’Oca, S.
  • Delmastro, C.
  • Lodeweyckx, S.
  • Corgnati, S.P.

Abstract

The primary objective of this paper is to demonstrate improved energy efficiency for domestic hot water (DHW) production in residential buildings. This is done by deriving data-driven optimal heating schedules (used interchangeably with policies) automatically. The optimization leverages actively learnt occupant behaviour and models for thermodynamics of the storage vessel to operate the heating mechanism – an air-source heat pump (ASHP) in this case – at the highest possible efficiency. The proposed algorithm, while tested on an ASHP, is essentially decoupled from the heating mechanism making it sufficiently robust to generalize to other types of heating mechanisms as well. Simulation results for this optimization based on data from 46 Net-Zero Energy Buildings (NZEB) in the Netherlands are presented. These show a reduction of energy consumption for DHW by 20% using a computationally inexpensive heuristic approach, and 27% when using a more intensive hybrid ant colony optimization based method. The energy savings are strongly dependent on occupant comfort level. This is demonstrated in real-world settings for a low-consumption house where active control was performed using heuristics for 3.5months and resulted in energy savings of 27% (61kWh). It is straightforward to extend the same models to perform automatic demand side management (ADSM) by treating the DHW vessel as a flexibility bearing device.

Suggested Citation

  • Kazmi, H. & D’Oca, S. & Delmastro, C. & Lodeweyckx, S. & Corgnati, S.P., 2016. "Generalizable occupant-driven optimization model for domestic hot water production in NZEB," Applied Energy, Elsevier, vol. 175(C), pages 1-15.
    • Handle: RePEc:eee:appene:v:175:y:2016:i:c:p:1-15
    DOI: 10.1016/j.apenergy.2016.04.108
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    Cited by:

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    3. Agnieszka Malec & Tomasz Cholewa & Alicja Siuta-Olcha, 2021. "Influence of Cold Water Inlets and Obstacles on the Energy Efficiency of the Hot Water Production Process in a Hot Water Storage Tank," Energies, MDPI, vol. 14(20), pages 1-26, October.
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    7. Gaetani, Isabella & Hoes, Pieter-Jan & Hensen, Jan L.M., 2018. "Estimating the influence of occupant behavior on building heating and cooling energy in one simulation run," Applied Energy, Elsevier, vol. 223(C), pages 159-171.
    8. Fouladvand, Javanshir & Aranguren Rojas, Maria & Hoppe, Thomas & Ghorbani, Amineh, 2022. "Simulating thermal energy community formation: Institutional enablers outplaying technological choice," Applied Energy, Elsevier, vol. 306(PA).
    9. Yildiz, Baran & Roberts, Mike & Bilbao, Jose I. & Heslop, Simon & Bruce, Anna & Dore, Jonathon & MacGill, Iain & Egan, Renate J. & Sproul, Alistair B., 2021. "Assessment of control tools for utilizing excess distributed photovoltaic generation in domestic electric water heating systems," Applied Energy, Elsevier, vol. 300(C).
    10. Delzendeh, Elham & Wu, Song & Lee, Angela & Zhou, Ying, 2017. "The impact of occupants’ behaviours on building energy analysis: A research review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1061-1071.
    11. Wang, Zhe & Hong, Tianzhen, 2020. "Reinforcement learning for building controls: The opportunities and challenges," Applied Energy, Elsevier, vol. 269(C).
    12. Ceusters, Glenn & Rodríguez, Román Cantú & García, Alberte Bouso & Franke, Rüdiger & Deconinck, Geert & Helsen, Lieve & Nowé, Ann & Messagie, Maarten & Camargo, Luis Ramirez, 2021. "Model-predictive control and reinforcement learning in multi-energy system case studies," Applied Energy, Elsevier, vol. 303(C).
    13. Pinto, Giuseppe & Piscitelli, Marco Savino & Vázquez-Canteli, José Ramón & Nagy, Zoltán & Capozzoli, Alfonso, 2021. "Coordinated energy management for a cluster of buildings through deep reinforcement learning," Energy, Elsevier, vol. 229(C).
    14. Heidari, Amirreza & Maréchal, François & Khovalyg, Dolaana, 2022. "An occupant-centric control framework for balancing comfort, energy use and hygiene in hot water systems: A model-free reinforcement learning approach," Applied Energy, Elsevier, vol. 312(C).
    15. Maltais, Louis-Gabriel & Gosselin, Louis, 2021. "Predictability analysis of domestic hot water consumption with neural networks: From single units to large residential buildings," Energy, Elsevier, vol. 229(C).
    16. Perera, A.T.D. & Kamalaruban, Parameswaran, 2021. "Applications of reinforcement learning in energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 137(C).
    17. Kazmi, Hussain & Mehmood, Fahad & Lodeweyckx, Stefan & Driesen, Johan, 2018. "Gigawatt-hour scale savings on a budget of zero: Deep reinforcement learning based optimal control of hot water systems," Energy, Elsevier, vol. 144(C), pages 159-168.
    18. Ibrahim Ali Kachalla & Christian Ghiaus, 2024. "Electric Water Boiler Energy Prediction: State-of-the-Art Review of Influencing Factors, Techniques, and Future Directions," Energies, MDPI, vol. 17(2), pages 1-32, January.

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