IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v283y2023ics0360544223018844.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544223018844
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2023.128490?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. 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).
    2. 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).
    3. 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).
    4. 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).
    5. 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.
    6. 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).
    7. 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).
    8. 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).
    9. 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).
    10. 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).
    11. 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.
    12. 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).
    13. 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).
    14. 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).
    15. 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).
    16. 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.
    17. 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).
    18. 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.
    19. 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.
    20. 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).
    21. 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.
    22. 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).
    Full references (including those not matched with items on IDEAS)

    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. Manfren, Massimiliano & James, Patrick AB. & Tronchin, Lamberto, 2022. "Data-driven building energy modelling – An analysis of the potential for generalisation through interpretable machine learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    2. Massimiliano Manfren & Karla M. Gonzalez-Carreon & Patrick A. B. James, 2024. "Interpretable Data-Driven Methods for Building Energy Modelling—A Review of Critical Connections and Gaps," Energies, MDPI, vol. 17(4), pages 1-22, February.
    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. 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).
    5. Sofia Agostinelli & Fabrizio Cumo & Meysam Majidi Nezhad & Giuseppe Orsini & Giuseppe Piras, 2022. "Renewable Energy System Controlled by Open-Source Tools and Digital Twin Model: Zero Energy Port Area in Italy," Energies, MDPI, vol. 15(5), pages 1-24, March.
    6. Li, Guannan & Li, Fan & Ahmad, Tanveer & Liu, Jiangyan & Li, Tao & Fang, Xi & Wu, Yubei, 2022. "Performance evaluation of sequence-to-sequence-Attention model for short-term multi-step ahead building energy predictions," Energy, Elsevier, vol. 259(C).
    7. Markus Fleschutz & Markus Bohlayer & Marco Braun & Michael D. Murphy, 2022. "Demand Response Analysis Framework (DRAF): An Open-Source Multi-Objective Decision Support Tool for Decarbonizing Local Multi-Energy Systems," Sustainability, MDPI, vol. 14(13), pages 1-38, June.
    8. Sofia Agostinelli & Fabrizio Cumo & Giambattista Guidi & Claudio Tomazzoli, 2021. "Cyber-Physical Systems Improving Building Energy Management: Digital Twin and Artificial Intelligence," Energies, MDPI, vol. 14(8), pages 1-25, April.
    9. Amal A. Al-Shargabi & Abdulbasit Almhafdy & Dina M. Ibrahim & Manal Alghieth & Francisco Chiclana, 2021. "Tuning Deep Neural Networks for Predicting Energy Consumption in Arid Climate Based on Buildings Characteristics," Sustainability, MDPI, vol. 13(22), pages 1-20, November.
    10. Fu, Yijun & Xu, Wei & Wang, Zhichao & Zhang, Shicong & Chen, Xi & Zhang, Xinyu, 2023. "Experimental study on thermoelectric effect pattern analysis and novel thermoelectric coupling model of BIPV facade system," Renewable Energy, Elsevier, vol. 217(C).
    11. Elzbieta Rynska & Joanna Klimowicz & Slawomir Kowal & Krzysztof Lyzwa & Michal Pierzchalski & Wojciech Rekosz, 2020. "Smart Energy Solutions as an Indispensable Multi-Criteria Input for a Coherent Urban Planning and Building Design Process—Two Case Studies for Smart Office Buildings in Warsaw Downtown Area," Energies, MDPI, vol. 13(15), pages 1-24, July.
    12. Liu, Che & Sun, Bo & Zhang, Chenghui & Li, Fan, 2020. "A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine," Applied Energy, Elsevier, vol. 275(C).
    13. Petter Arnesen & Odd A. Hjelkrem, 2018. "An Estimator for Traffic Breakdown Probability Based on Classification of Transitional Breakdown Events," Transportation Science, INFORMS, vol. 52(3), pages 593-602, June.
    14. Dehler-Holland, Joris & Schumacher, Kira & Fichtner, Wolf, 2021. "Topic Modeling Uncovers Shifts in Media Framing of the German Renewable Energy Act," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 2(1).
    15. Saima Akhtar & Sulman Shahzad & Asad Zaheer & Hafiz Sami Ullah & Heybet Kilic & Radomir Gono & Michał Jasiński & Zbigniew Leonowicz, 2023. "Short-Term Load Forecasting Models: A Review of Challenges, Progress, and the Road Ahead," Energies, MDPI, vol. 16(10), pages 1-29, May.
    16. Malte Willmes & Katherine M Ransom & Levi S Lewis & Christian T Denney & Justin J G Glessner & James A Hobbs, 2018. "IsoFishR: An application for reproducible data reduction and analysis of strontium isotope ratios (87Sr/86Sr) obtained via laser-ablation MC-ICP-MS," PLOS ONE, Public Library of Science, vol. 13(9), pages 1-15, September.
    17. Stinner, Sebastian & Schlösser, Tim & Huchtemann, Kristian & Müller, Dirk & Monti, Antonello, 2017. "Primary energy evaluation of heat pumps considering dynamic boundary conditions in the energy system," Energy, Elsevier, vol. 138(C), pages 60-78.
    18. Rongjiang Ma & Shen Yang & Xianlin Wang & Xi-Cheng Wang & Ming Shan & Nanyang Yu & Xudong Yang, 2020. "Systematic Method for the Energy-Saving Potential Calculation of Air-Conditioning Systems via Data Mining. Part I: Methodology," Energies, MDPI, vol. 14(1), pages 1-15, December.
    19. Fachrizal, Reza & Shepero, Mahmoud & Åberg, Magnus & Munkhammar, Joakim, 2022. "Optimal PV-EV sizing at solar powered workplace charging stations with smart charging schemes considering self-consumption and self-sufficiency balance," Applied Energy, Elsevier, vol. 307(C).
    20. Juan Pablo Fernández Goycoolea & Gabriela Zapata-Lancaster & Christopher Whitman, 2022. "Operational Emissions in Prosuming Dwellings: A Study Comparing Different Sources of Grid CO 2 Intensity Values in South Wales, UK," Energies, MDPI, vol. 15(7), pages 1-24, March.

    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:283:y:2023:i:c:s0360544223018844. 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.