IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v352y2023ics0306261923013351.html
   My bibliography  Save this article

Estimating electric power consumption of in-situ residential heat pump systems: A data-driven approach

Author

Listed:
  • Song, Yang
  • Peskova, Monika
  • Rolando, Davide
  • Zucker, Gerhard
  • Madani, Hatef

Abstract

International Energy Agency predicts that the global number of installed heat pumps (HP) will increase from 180 million in 2020 to approximately 600 million by 2030, covering 20% of buildings heating needs. Electric power consumption is one of the main key performance indicators for the heat pump systems from techno-economic perspective. However a common issue prevalent in many existing heat pumps is the lack of electric power measurement. The modern installations might be equipped with electric power measurement sensors but this comes at a higher system cost for the manufacturers and end-users. The primary objective of this work is to propose a virtual measurement for estimating power consumption, thereby eliminating the need for field measurement of power for heat pumps. To achieve the objective, a data-driven approach is proposed. Firstly, the in-situ data is preprocessed through data merging, cleaning, and normalization. Then, input features are pre-selected using Spearman correlation coefficients, and further refined by addressing multicollinearity problem. Following this, Extreme Gradient Boosting (XGBoost) models and polynomial models are developed by considering different features as inputs. All models are finally validated against the in-situ data from multi-units of ground source heat pump (GSHP) and air source heat pump (ASHP) installations. The results showed that the electric power consumption of GSHP can be estimated with high accuracy (99% for R2, 10 W for MAE, and 1% for MAPE) through generic data-driven models using only four easy-to-measure input features. Taking three input features as inputs for ASHP generic model, the accuracy can be reached to 83% for R2, 125 W for MAE, and 9% for MAPE. The method presented in this paper can be applied to estimate power consumption of millions of heat pumps and consequently add a significant value as well as provide different types of services, such as cost-saving benefits for manufacturers and end-users, flexibility services for aggregators and electricity grids.

Suggested Citation

  • Song, Yang & Peskova, Monika & Rolando, Davide & Zucker, Gerhard & Madani, Hatef, 2023. "Estimating electric power consumption of in-situ residential heat pump systems: A data-driven approach," Applied Energy, Elsevier, vol. 352(C).
    • Handle: RePEc:eee:appene:v:352:y:2023:i:c:s0306261923013351
    DOI: 10.1016/j.apenergy.2023.121971
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923013351
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2023.121971?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. Tangwe, Stephen & Simon, Michael & Meyer, Edson, 2014. "Mathematical modeling and simulation application to visualize the performance of retrofit heat pump water heater under first hour heating rating," Renewable Energy, Elsevier, vol. 72(C), pages 203-211.
    2. Zhong, Wei & Huang, Wei & Lin, Xiaojie & Li, Zhongbo & Zhou, Yi, 2020. "Research on data-driven identification and prediction of heat response time of urban centralized heating system," Energy, Elsevier, vol. 212(C).
    3. Rissman, Jeffrey & Bataille, Chris & Masanet, Eric & Aden, Nate & Morrow, William R. & Zhou, Nan & Elliott, Neal & Dell, Rebecca & Heeren, Niko & Huckestein, Brigitta & Cresko, Joe & Miller, Sabbie A., 2020. "Technologies and policies to decarbonize global industry: Review and assessment of mitigation drivers through 2070," Applied Energy, Elsevier, vol. 266(C).
    4. Baik, Young-Jin & Kim, Minsung & Chang, Ki-Chang & Lee, Young-Soo & Ra, Ho-Sang, 2014. "Potential to enhance performance of seawater-source heat pump by series operation," Renewable Energy, Elsevier, vol. 65(C), pages 236-244.
    5. Simon, F. & Ordoñez, J. & Reddy, T.A. & Girard, A. & Muneer, T., 2016. "Developing multiple regression models from the manufacturer's ground-source heat pump catalogue data," Renewable Energy, Elsevier, vol. 95(C), pages 413-421.
    6. Ciulla, G. & D'Amico, A., 2019. "Building energy performance forecasting: A multiple linear regression approach," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    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. Semmelmann, Leo & Hertel, Matthias & Kircher, Kevin J. & Mikut, Ralf & Hagenmeyer, Veit & Weinhardt, Christof, 2024. "The impact of heat pumps on day-ahead energy community load forecasting," Applied Energy, Elsevier, vol. 368(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. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
    2. Arkar, C. & Žižak, T. & Domjan, S. & Medved, S., 2020. "Dynamic parametric models for the holistic evaluation of semi-transparent photovoltaic/thermal façade with latent storage inserts," Applied Energy, Elsevier, vol. 280(C).
    3. Fábio T. F. Silva & Alexandre Szklo & Amanda Vinhoza & Ana Célia Nogueira & André F. P. Lucena & Antônio Marcos Mendonça & Camilla Marcolino & Felipe Nunes & Francielle M. Carvalho & Isabela Tagomori , 2022. "Inter-sectoral prioritization of climate technologies: insights from a Technology Needs Assessment for mitigation in Brazil," Mitigation and Adaptation Strategies for Global Change, Springer, vol. 27(7), pages 1-39, October.
    4. Qi, Meng & Park, Jinwoo & Lee, Inkyu & Moon, Il, 2022. "Liquid air as an emerging energy vector towards carbon neutrality: A multi-scale systems perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
    5. Mayyas Alsalman & Vian Ahmed & Zied Bahroun & Sara Saboor, 2023. "An Economic Analysis of Solar Energy Generation Policies in the UAE," Energies, MDPI, vol. 16(7), pages 1-25, March.
    6. Róbert Csalódi & Tímea Czvetkó & Viktor Sebestyén & János Abonyi, 2022. "Sectoral Analysis of Energy Transition Paths and Greenhouse Gas Emissions," Energies, MDPI, vol. 15(21), pages 1-26, October.
    7. Al-Qahtani, Amjad & Parkinson, Brett & Hellgardt, Klaus & Shah, Nilay & Guillen-Gosalbez, Gonzalo, 2021. "Uncovering the true cost of hydrogen production routes using life cycle monetisation," Applied Energy, Elsevier, vol. 281(C).
    8. Stamatios K. Chrysikopoulos & Panos T. Chountalas & Dimitrios A. Georgakellos & Athanasios G. Lagodimos, 2024. "Decarbonization in the Oil and Gas Sector: The Role of Power Purchase Agreements and Renewable Energy Certificates," Sustainability, MDPI, vol. 16(15), pages 1-24, July.
    9. Pashchenko, Dmitry, 2023. "Hydrogen-rich gas as a fuel for the gas turbines: A pathway to lower CO2 emission," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    10. Ole Øiene Smedegård & Thomas Jonsson & Bjørn Aas & Jørn Stene & Laurent Georges & Salvatore Carlucci, 2021. "The Implementation of Multiple Linear Regression for Swimming Pool Facilities: Case Study at Jøa, Norway," Energies, MDPI, vol. 14(16), pages 1-24, August.
    11. Landon Yoder & Alora Cain & Ananya Rao & Nathaniel Geiger & Ben Kravitz & Mack Mercer & Deidra Miniard & Sangeet Nepal & Thomas Nunn & Mary Sluder & Grace Weiler & Shahzeen Z. Attari, 2024. "Muddling through Climate Change: A Qualitative Exploration of India and U.S. Climate Experts’ Perspectives on Solutions, Pathways, and Barriers," Sustainability, MDPI, vol. 16(13), pages 1-20, June.
    12. Ana Ferreira & Manuel Duarte Pinheiro & Jorge de Brito & Ricardo Mateus, 2022. "Embodied vs. Operational Energy and Carbon in Retail Building Shells: A Case Study in Portugal," Energies, MDPI, vol. 16(1), pages 1-23, December.
    13. Paltsev, Sergey & Morris, Jennifer & Kheshgi, Haroon & Herzog, Howard, 2021. "Hard-to-Abate Sectors: The role of industrial carbon capture and storage (CCS) in emission mitigation," Applied Energy, Elsevier, vol. 300(C).
    14. Józef Paska & Tomasz Surma & Paweł Terlikowski & Krzysztof Zagrajek, 2020. "Electricity Generation from Renewable Energy Sources in Poland as a Part of Commitment to the Polish and EU Energy Policy," Energies, MDPI, vol. 13(16), pages 1-31, August.
    15. Pérez-Sánchez, Laura À. & Velasco-Fernández, Raúl & Giampietro, Mario, 2022. "Factors and actions for the sustainability of the residential sector. The nexus of energy, materials, space, and time use," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    16. Sun, Chunhua & Liu, Yiting & Cao, Shanshan & Chen, Jiali & Xia, Guoqiang & Wu, Xiangdong, 2022. "Identification of control regularity of heating stations based on cross-correlation function dynamic time delay method," Energy, Elsevier, vol. 246(C).
    17. Zhang, Weike & Fan, Hongxia & Zhao, Qiwei, 2023. "Seeing green: How does digital infrastructure affect carbon emission intensity?," Energy Economics, Elsevier, vol. 127(PB).
    18. Alessandro Rosengart & Maja Granzotto & Rudi Wierer & Gianluca Pazzaglia & Alessandro Salvi & Giovanni Dotelli, 2023. "The Green Value Engineering Methodology: A Sustainability-Driven Project Management Tool for Capital Projects in Process Industry," Sustainability, MDPI, vol. 15(20), pages 1-21, October.
    19. Meenakshi Sharma & Rajesh Kaushal & Prashant Kaushik & Seeram Ramakrishna, 2021. "Carbon Farming: Prospects and Challenges," Sustainability, MDPI, vol. 13(19), pages 1-15, October.
    20. Lumbreras, Mikel & Garay-Martinez, Roberto & Arregi, Beñat & Martin-Escudero, Koldobika & Diarce, Gonzalo & Raud, Margus & Hagu, Indrek, 2022. "Data driven model for heat load prediction in buildings connected to District Heating by using smart heat meters," Energy, Elsevier, vol. 239(PD).

    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:appene:v:352:y:2023:i:c:s0306261923013351. 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.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.