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Interpretable data-driven building load profiles modelling for Measurement and Verification 2.0

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  • Manfren, Massimiliano
  • Nastasi, Benedetto

Abstract

Accelerating the decarbonisation of the built environment necessitates increasing electrification of end-uses, which in turn poses the issue of rethinking the role of energy efficiency in conjunction with flexibility in grid interaction. This requires a better understanding of the electricity load profiles at hourly or sub-hourly intervals using techniques that are simple, reliable, and interpretable. To this extent, this study proposes a reformulation of the Time Of Week and Temperature modelling approach. This approach is able to separate the energy consumption dependence on building operational characteristics (Time Of Week) and on weather (outdoor air temperature), through a highly automated modelling workflow, necessitating minimal effort for model tuning. These features, along with its intrinsic interpretability due to its formulation using multivariate regression and the availability of open-source software, makes it an ideal starting point for applied research. The case study selected for the research is a fully electrified public building in Southern Italy. The building has been monitored for 5 years, before, during and after the COVID-19 lockdown. The novel model formulation is calibrated using hourly interval data with a Coefficient of Variation of Root Mean Square Error in the range of 20.0–28.5% throughout the various monitoring periods. The counterfactual analysis of electricity consumption indicates a 10.7–26.7% decrease in electricity consumption due to operational adjustments following COVID-19 lockdown, highlighting the impact of behavioural change. Finally, the possibility of additional workflow automation and enhanced interpretability is discussed.

Suggested Citation

  • Manfren, Massimiliano & Nastasi, Benedetto, 2023. "Interpretable data-driven building load profiles modelling for Measurement and Verification 2.0," Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:energy:v:283:y:2023:i:c:s0360544223018844
    DOI: 10.1016/j.energy.2023.128490
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    References listed on IDEAS

    as
    1. Killick, Rebecca & Eckley, Idris A., 2014. "changepoint: An R Package for Changepoint Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 58(i03).
    2. Plaga, Leonie Sara & Bertsch, Valentin, 2023. "Methods for assessing climate uncertainty in energy system models — A systematic literature review," Applied Energy, Elsevier, vol. 331(C).
    3. Liang, Xinbin & Zhu, Xu & Chen, Kang & Chen, Siliang & Jin, Xinqiao & Du, Zhimin, 2023. "Endowing data-driven models with rejection ability: Out-of-distribution detection and confidence estimation for black-box models of building energy systems," Energy, Elsevier, vol. 263(PC).
    4. Zhang, Xiaofeng & Kong, Xiaoying & Yan, Renshi & Liu, Yuting & Xia, Peng & Sun, Xiaoqin & Zeng, Rong & Li, Hongqiang, 2023. "Data-driven cooling, heating and electrical load prediction for building integrated with electric vehicles considering occupant travel behavior," Energy, Elsevier, vol. 264(C).
    5. D’Ettorre, F. & Banaei, M. & Ebrahimy, R. & Pourmousavi, S. Ali & Blomgren, E.M.V. & Kowalski, J. & Bohdanowicz, Z. & Łopaciuk-Gonczaryk, B. & Biele, C. & Madsen, H., 2022. "Exploiting demand-side flexibility: State-of-the-art, open issues and social perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    6. Haas, Reinhard & Duic, Neven & Auer, Hans & Ajanovic, Amela & Ramsebner, Jasmine & Knapek, Jaroslav & Zwickl-Bernhard, Sebastian, 2023. "The photovoltaic revolution is on: How it will change the electricity system in a lasting way," Energy, Elsevier, vol. 265(C).
    7. 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.
    8. Park, Somin & Shim, Jisoo & Song, Doosam, 2021. "Issues in calculation of balance-point temperatures for heating degree-days for the development of building-energy policy," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    9. Albana Kona & Paolo Bertoldi & Şiir Kılkış, 2019. "Covenant of Mayors: Local Energy Generation, Methodology, Policies and Good Practice Examples," Energies, MDPI, vol. 12(6), pages 1-29, March.
    10. Zhang, Yan & Teoh, Bak Koon & Wu, Maozhi & Chen, Jiayu & Zhang, Limao, 2023. "Data-driven estimation of building energy consumption and GHG emissions using explainable artificial intelligence," Energy, Elsevier, vol. 262(PA).
    11. Manfren, Massimiliano & Nastasi, Benedetto & Groppi, Daniele & Astiaso Garcia, Davide, 2020. "Open data and energy analytics - An analysis of essential information for energy system planning, design and operation," Energy, Elsevier, vol. 213(C).
    12. Sun, Ying & Haghighat, Fariborz & Fung, Benjamin C.M., 2022. "Trade-off between accuracy and fairness of data-driven building and indoor environment models: A comparative study of pre-processing methods," Energy, Elsevier, vol. 239(PD).
    13. Omarov, Bekarys & Memon, Shazim Ali & Kim, Jong, 2023. "A novel approach to develop climate classification based on degree days and building energy performance," Energy, Elsevier, vol. 267(C).
    14. Salom, Jaume & Marszal, Anna Joanna & Widén, Joakim & Candanedo, José & Lindberg, Karen Byskov, 2014. "Analysis of load match and grid interaction indicators in net zero energy buildings with simulated and monitored data," Applied Energy, Elsevier, vol. 136(C), pages 119-131.
    15. Manfren, Massimiliano & Nastasi, Benedetto & Tronchin, Lamberto & Groppi, Daniele & Garcia, Davide Astiaso, 2021. "Techno-economic analysis and energy modelling as a key enablers for smart energy services and technologies in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    16. Fan, Cheng & Lei, Yutian & Sun, Yongjun & Piscitelli, Marco Savino & Chiosa, Roberto & Capozzoli, Alfonso, 2022. "Data-centric or algorithm-centric: Exploiting the performance of transfer learning for improving building energy predictions in data-scarce context," Energy, Elsevier, vol. 240(C).
    17. Leiria, Daniel & Johra, Hicham & Marszal-Pomianowska, Anna & Pomianowski, Michal Zbigniew, 2023. "A methodology to estimate space heating and domestic hot water energy demand profile in residential buildings from low-resolution heat meter data," Energy, Elsevier, vol. 263(PB).
    18. Bollinger, L.A. & Davis, C.B. & Evins, R. & Chappin, E.J.L. & Nikolic, I., 2018. "Multi-model ecologies for shaping future energy systems: Design patterns and development paths," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3441-3451.
    19. Fu, Hongxiang & Baltazar, Juan-Carlos & Claridge, David E., 2021. "Review of developments in whole-building statistical energy consumption models for commercial buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    20. Francesco Mancini & Gianluigi Lo Basso, 2020. "How Climate Change Affects the Building Energy Consumptions Due to Cooling, Heating, and Electricity Demands of Italian Residential Sector," Energies, MDPI, vol. 13(2), pages 1-24, January.
    21. Naji, Sareh & Keivani, Afram & Shamshirband, Shahaboddin & Alengaram, U. Johnson & Jumaat, Mohd Zamin & Mansor, Zulkefli & Lee, Malrey, 2016. "Estimating building energy consumption using extreme learning machine method," Energy, Elsevier, vol. 97(C), pages 506-516.
    22. Lizana, Jesus & Halloran, Claire E. & Wheeler, Scot & Amghar, Nabil & Renaldi, Renaldi & Killendahl, Markus & Perez-Maqueda, Luis A. & McCulloch, Malcolm & Chacartegui, Ricardo, 2023. "A national data-based energy modelling to identify optimal heat storage capacity to support heating electrification," Energy, Elsevier, vol. 262(PA).
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