IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v278y2023ipas0360544223012331.html
   My bibliography  Save this article

Application of a large smart thermostat dataset for model calibration and Model Predictive Control implementation in the residential sector

Author

Listed:
  • Vallianos, Charalampos
  • Candanedo, José
  • Athienitis, Andreas

Abstract

Electrification of space heating combined with electricity generation through renewable resources has a significant potential to reduce greenhouse gas emissions, especially when coupled with advanced control strategies that utilize building energy flexibility. This study analyses data from approximately 7,800 houses in Ontario and Québec, Canada to investigate the most suitable data length, data interval and calibration horizon of building models for assessment of Model Predictive Control strategies on a large scale. Overall, models with calibration horizon of 24 h, data length of 7 days and time interval of 15 min provided the best balance between accuracy and computational resources. The calibrated models were used to compare a simple deadband controller to a Model Predictive Controller that minimizes the cost of electricity for a cold day in January. The results showed that the Model Predictive Controller can systematically reduce the high-price energy consumption, while improving thermal comfort, by successfully preheating before the high-price periods. Preheating shifts the peak power consumption, reducing it by 15 % for Ontario and 30 % for Québec. If just 1,000 houses adopt optimal strategies, the grid could see an average reduction in energy consumption of up to 15 MWh and 11 MWh during high-price periods in Ontario and Québec respectively.

Suggested Citation

  • Vallianos, Charalampos & Candanedo, José & Athienitis, Andreas, 2023. "Application of a large smart thermostat dataset for model calibration and Model Predictive Control implementation in the residential sector," Energy, Elsevier, vol. 278(PA).
    • Handle: RePEc:eee:energy:v:278:y:2023:i:pa:s0360544223012331
    DOI: 10.1016/j.energy.2023.127839
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223012331
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2023.127839?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Sengupta, Manajit & Xie, Yu & Lopez, Anthony & Habte, Aron & Maclaurin, Galen & Shelby, James, 2018. "The National Solar Radiation Data Base (NSRDB)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 89(C), pages 51-60.
    2. Harish, V.S.K.V. & Kumar, Arun, 2016. "A review on modeling and simulation of building energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 1272-1292.
    3. Lund, Peter D. & Lindgren, Juuso & Mikkola, Jani & Salpakari, Jyri, 2015. "Review of energy system flexibility measures to enable high levels of variable renewable electricity," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 785-807.
    4. Žáčeková, Eva & Váňa, Zdeněk & Cigler, Jiří, 2014. "Towards the real-life implementation of MPC for an office building: Identification issues," Applied Energy, Elsevier, vol. 135(C), pages 53-62.
    5. Rhodes, Joshua D. & Cole, Wesley J. & Upshaw, Charles R. & Edgar, Thomas F. & Webber, Michael E., 2014. "Clustering analysis of residential electricity demand profiles," Applied Energy, Elsevier, vol. 135(C), pages 461-471.
    6. Foucquier, Aurélie & Robert, Sylvain & Suard, Frédéric & Stéphan, Louis & Jay, Arnaud, 2013. "State of the art in building modelling and energy performances prediction: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 272-288.
    7. Haider, Haider Tarish & See, Ong Hang & Elmenreich, Wilfried, 2016. "A review of residential demand response of smart grid," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 166-178.
    8. Bianchini, Gianni & Casini, Marco & Vicino, Antonio & Zarrilli, Donato, 2016. "Demand-response in building heating systems: A Model Predictive Control approach," Applied Energy, Elsevier, vol. 168(C), pages 159-170.
    9. Seal, Sayani & Boulet, Benoit & Dehkordi, Vahid R., 2020. "Centralized model predictive control strategy for thermal comfort and residential energy management," Energy, Elsevier, vol. 212(C).
    10. White, Philip R. & Rhodes, Joshua D. & Wilson, Eric J.H. & Webber, Michael E., 2021. "Quantifying the impact of residential space heating electrification on the Texas electric grid," Applied Energy, Elsevier, vol. 298(C).
    11. Li, Yanfei & O'Neill, Zheng & Zhang, Liang & Chen, Jianli & Im, Piljae & DeGraw, Jason, 2021. "Grey-box modeling and application for building energy simulations - A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 146(C).
    12. Oliveira Panão, Marta J.N. & Mateus, Nuno M. & Carrilho da Graça, G., 2019. "Measured and modeled performance of internal mass as a thermal energy battery for energy flexible residential buildings," Applied Energy, Elsevier, vol. 239(C), pages 252-267.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Wenxiao Chu & Maria Vicidomini & Francesco Calise & Neven Duić & Poul Alberg Østergaard & Qiuwang Wang & Maria da Graça Carvalho, 2023. "Review of Hot Topics in the Sustainable Development of Energy, Water, and Environment Systems Conference in 2022," Energies, MDPI, vol. 16(23), pages 1-20, December.
    2. Petrucci, Andrea & Ayevide, Follivi Kloutse & Buonomano, Annamaria & Athienitis, Andreas, 2023. "Development of energy aggregators for virtual communities: The energy efficiency-flexibility nexus for demand response," Renewable Energy, Elsevier, vol. 215(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zhan, Sicheng & Chong, Adrian, 2021. "Data requirements and performance evaluation of model predictive control in buildings: A modeling perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
    2. Pallonetto, Fabiano & De Rosa, Mattia & D’Ettorre, Francesco & Finn, Donal P., 2020. "On the assessment and control optimisation of demand response programs in residential buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
    3. Massimiliano Manfren & Maurizio Sibilla & Lamberto Tronchin, 2021. "Energy Modelling and Analytics in the Built Environment—A Review of Their Role for Energy Transitions in the Construction Sector," Energies, MDPI, vol. 14(3), pages 1-29, January.
    4. Yildiz, B. & Bilbao, J.I. & Sproul, A.B., 2017. "A review and analysis of regression and machine learning models on commercial building electricity load forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1104-1122.
    5. Gokhale, Gargya & Claessens, Bert & Develder, Chris, 2022. "Physics informed neural networks for control oriented thermal modeling of buildings," Applied Energy, Elsevier, vol. 314(C).
    6. Gautham Krishnadas & Aristides Kiprakis, 2020. "A Machine Learning Pipeline for Demand Response Capacity Scheduling," Energies, MDPI, vol. 13(7), pages 1-25, April.
    7. Li, Yanxue & Wang, Zixuan & Xu, Wenya & Gao, Weijun & Xu, Yang & Xiao, Fu, 2023. "Modeling and energy dynamic control for a ZEH via hybrid model-based deep reinforcement learning," Energy, Elsevier, vol. 277(C).
    8. Camille Pajot & Nils Artiges & Benoit Delinchant & Simon Rouchier & Frédéric Wurtz & Yves Maréchal, 2019. "An Approach to Study District Thermal Flexibility Using Generative Modeling from Existing Data," Energies, MDPI, vol. 12(19), pages 1-22, September.
    9. Pinto, Giuseppe & Deltetto, Davide & Capozzoli, Alfonso, 2021. "Data-driven district energy management with surrogate models and deep reinforcement learning," Applied Energy, Elsevier, vol. 304(C).
    10. Harrison-Atlas, Dylan & Murphy, Caitlin & Schleifer, Anna & Grue, Nicholas, 2022. "Temporal complementarity and value of wind-PV hybrid systems across the United States," Renewable Energy, Elsevier, vol. 201(P1), pages 111-123.
    11. Di Natale, L. & Svetozarevic, B. & Heer, P. & Jones, C.N., 2023. "Towards scalable physically consistent neural networks: An application to data-driven multi-zone thermal building models," Applied Energy, Elsevier, vol. 340(C).
    12. Gorman, Will & Barbose, Galen & Pablo Carvallo, Juan & Baik, Sunhee & Miller, Chandler & White, Philip & Praprost, Marlena, 2023. "County-level assessment of behind-the-meter solar and storage to mitigate long duration power interruptions for residential customers," Applied Energy, Elsevier, vol. 342(C).
    13. Wang, Qiaochu & Ding, Yan & Kong, Xiangfei & Tian, Zhe & Xu, Linrui & He, Qing, 2022. "Load pattern recognition based optimization method for energy flexibility in office buildings," Energy, Elsevier, vol. 254(PC).
    14. Zhang, Yang & Campana, Pietro Elia & Yang, Ying & Stridh, Bengt & Lundblad, Anders & Yan, Jinyue, 2018. "Energy flexibility from the consumer: Integrating local electricity and heat supplies in a building," Applied Energy, Elsevier, vol. 223(C), pages 430-442.
    15. Ohlsson, K.E. Anders & Nair, Gireesh & Olofsson, Thomas, 2022. "Uncertainty in model prediction of energy savings in building retrofits: Case of thermal transmittance of windows," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    16. Zahra Fallahi & Gregor P. Henze, 2019. "Interactive Buildings: A Review," Sustainability, MDPI, vol. 11(14), pages 1-26, July.
    17. Heegang Kim & Myoungsouk Yeo, 2020. "Thermal Bridge Modeling and a Dynamic Analysis Method Using the Analogy of a Steady-State Thermal Bridge Analysis and System Identification Process for Building Energy Simulation: Methodology and Vali," Energies, MDPI, vol. 13(17), pages 1-22, August.
    18. Ziras, Charalampos & Heinrich, Carsten & Pertl, Michael & Bindner, Henrik W., 2019. "Experimental flexibility identification of aggregated residential thermal loads using behind-the-meter data," Applied Energy, Elsevier, vol. 242(C), pages 1407-1421.
    19. Ferracuti, Francesco & Fonti, Alessandro & Ciabattoni, Lucio & Pizzuti, Stefano & Arteconi, Alessia & Helsen, Lieve & Comodi, Gabriele, 2017. "Data-driven models for short-term thermal behaviour prediction in real buildings," Applied Energy, Elsevier, vol. 204(C), pages 1375-1387.
    20. Xing Shi & Binghui Si & Jiangshan Zhao & Zhichao Tian & Chao Wang & Xing Jin & Xin Zhou, 2019. "Magnitude, Causes, and Solutions of the Performance Gap of Buildings: A Review," Sustainability, MDPI, vol. 11(3), pages 1-21, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:energy:v:278:y:2023:i:pa:s0360544223012331. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.