IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i18p5036-d1755072.html

Virtual Energy Replication Framework for Predicting Residential PV Power, Heat Pump Load, and Thermal Comfort Using Weather Forecast Data

Author

Listed:
  • Daud Mustafa Minhas

    (Industrial Security Lab, ZeMA—Center for Mechatronics and Automation Technology, D-66121 Saarbrucken, Germany)

  • Muhammad Usman

    (Mechanical Engineering Department, University of Engineering and Technology Taxila, Taxila 47050, Pakistan)

  • Irtaza Bashir Raja

    (College of Electrical and Mechanical Engineering, National University of Sciences and Technology, Islamabad 44000, Pakistan)

  • Aneela Wakeel

    (Mechanical Engineering Department, University of Engineering and Technology Taxila, Taxila 47050, Pakistan)

  • Muzaffar Ali

    (Mechanical Engineering Department, Faculty of Engineering, Kocaeli University, 41001 Kocaeli, Türkiye)

  • Georg Frey

    (Chair of Automation and Energy Systems, Saarland University, D-66123 Saarbrücken, Germany)

Abstract

It is essential to balance energy supply and demand in residential buildings through accurate forecasting of energy use due to varying daily and seasonal residential building loads. This study demonstrates a data-driven Virtual Energy Replication Framework (VERF) to predict the behavior of residential buildings using weather forecast data. The framework integrates supervised machine learning models and time-ahead weather parameters to estimate photovoltaic (PV) power production, heat pump energy consumption, and indoor thermal comfort. The accuracy of prediction models is validated using TRNSYS simulations of a typical household in Saarbrucken, Germany, a temperate oceanic climate region. The XGBoost model exhibits the highest reliability, achieving a root mean square error (RMSE) of 0.003 kW for PV power generation and 0.025 kW for heat pump energy use, with R 2 scores of 0.94 and 0.87, respectively. XGBoost and random forest regression models perform well in predicting PV generation and HP electricity load, with mean prediction errors of 5.27–6% and 0–7.7%, respectively. In addition, the thermal comfort index (PPD) is predicted with an RMSE of 1.84 kW and an R 2 score of 0.80 using the XGBoost model. The mean prediction error remains between 2.4% (XGBoost regression) and −11.5% (lasso regression) throughout the forecasted data. Because the framework requires no real-time instrumentation or detailed energy modelling, it is scalable and adaptable for smart building energy systems, and has particular value for Building-Integrated Photovoltaics (BIPV) demonstration projects on account of its predictive load-matching capabilities. The research findings justify the applicability of VERF for efficient and sustainable energy management using weather-informed prediction models in residential buildings.

Suggested Citation

  • Daud Mustafa Minhas & Muhammad Usman & Irtaza Bashir Raja & Aneela Wakeel & Muzaffar Ali & Georg Frey, 2025. "Virtual Energy Replication Framework for Predicting Residential PV Power, Heat Pump Load, and Thermal Comfort Using Weather Forecast Data," Energies, MDPI, vol. 18(18), pages 1-20, September.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:18:p:5036-:d:1755072
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/18/5036/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/18/18/5036/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Joanna Henzel & Łukasz Wróbel & Marcin Fice & Marek Sikora, 2022. "Energy Consumption Forecasting for the Digital-Twin Model of the Building," Energies, MDPI, vol. 15(12), pages 1-21, June.
    2. Viorica Rozina Chifu & Tudor Cioara & Cristina Bianca Pop & Ionut Anghel & Andrei Pelle, 2024. "Physics-Informed Neural Networks for Heat Pump Load Prediction," Energies, MDPI, vol. 18(1), pages 1-20, December.
    3. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.
    4. Wang, Zixuan & Xiao, Fu & Ran, Yi & Li, Yanxue & Xu, Yang, 2024. "Scalable energy management approach of residential hybrid energy system using multi-agent deep reinforcement learning," Applied Energy, Elsevier, vol. 367(C).
    5. Lee, Sang Hoon & Hong, Tianzhen & Piette, Mary Ann & Taylor-Lange, Sarah C., 2015. "Energy retrofit analysis toolkits for commercial buildings: A review," Energy, Elsevier, vol. 89(C), pages 1087-1100.
    6. Wang, Zeyu & Srinivasan, Ravi S., 2017. "A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 796-808.
    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. Grillone, Benedetto & Danov, Stoyan & Sumper, Andreas & Cipriano, Jordi & Mor, Gerard, 2020. "A review of deterministic and data-driven methods to quantify energy efficiency savings and to predict retrofitting scenarios in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    2. Tian, Shen & Shao, Shuangquan & Liu, Bin, 2019. "Investigation on transient energy consumption of cold storages: Modeling and a case study," Energy, Elsevier, vol. 180(C), pages 1-9.
    3. Fan, Cheng & Sun, Yongjun & Xiao, Fu & Ma, Jie & Lee, Dasheng & Wang, Jiayuan & Tseng, Yen Chieh, 2020. "Statistical investigations of transfer learning-based methodology for short-term building energy predictions," Applied Energy, Elsevier, vol. 262(C).
    4. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    5. Gautham Krishnadas & Aristides Kiprakis, 2020. "A Machine Learning Pipeline for Demand Response Capacity Scheduling," Energies, MDPI, vol. 13(7), pages 1-25, April.
    6. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A novel improved model for building energy consumption prediction based on model integration," Applied Energy, Elsevier, vol. 262(C).
    7. Li, Guannan & Wu, Yubei & Yoon, Sungmin & Fang, Xi, 2024. "Comprehensive transferability assessment of short-term cross-building-energy prediction using deep adversarial network transfer learning," Energy, Elsevier, vol. 299(C).
    8. Guillaume Guerard & Hugo Pousseur & Ihab Taleb, 2021. "Isolated Areas Consumption Short-Term Forecasting Method," Energies, MDPI, vol. 14(23), pages 1-23, November.
    9. Finck, Christian & Li, Rongling & Zeiler, Wim, 2020. "Optimal control of demand flexibility under real-time pricing for heating systems in buildings: A real-life demonstration," Applied Energy, Elsevier, vol. 263(C).
    10. Zhou, Xinlei & Lin, Wenye & Kumar, Ritunesh & Cui, Ping & Ma, Zhenjun, 2022. "A data-driven strategy using long short term memory models and reinforcement learning to predict building electricity consumption," Applied Energy, Elsevier, vol. 306(PB).
    11. Liang, Xinbin & Chen, Siliang & Zhu, Xu & Jin, Xinqiao & Du, Zhimin, 2023. "Domain knowledge decomposition of building energy consumption and a hybrid data-driven model for 24-h ahead predictions," Applied Energy, Elsevier, vol. 344(C).
    12. Sunil Kumar Mohapatra & Sushruta Mishra & Hrudaya Kumar Tripathy & Akash Kumar Bhoi & Paolo Barsocchi, 2021. "A Pragmatic Investigation of Energy Consumption and Utilization Models in the Urban Sector Using Predictive Intelligence Approaches," Energies, MDPI, vol. 14(13), pages 1-28, June.
    13. Niemierko, Rochus & Töppel, Jannick & Tränkler, Timm, 2019. "A D-vine copula quantile regression approach for the prediction of residential heating energy consumption based on historical data," Applied Energy, Elsevier, vol. 233, pages 691-708.
    14. Gao, Lei & Liu, Tianyuan & Cao, Tao & Hwang, Yunho & Radermacher, Reinhard, 2021. "Comparing deep learning models for multi energy vectors prediction on multiple types of building," Applied Energy, Elsevier, vol. 301(C).
    15. Bampoulas, Adamantios & Pallonetto, Fabiano & Mangina, Eleni & Finn, Donal P., 2022. "An ensemble learning-based framework for assessing the energy flexibility of residential buildings with multicomponent energy systems," Applied Energy, Elsevier, vol. 315(C).
    16. Jason Runge & Radu Zmeureanu, 2021. "A Review of Deep Learning Techniques for Forecasting Energy Use in Buildings," Energies, MDPI, vol. 14(3), pages 1-26, January.
    17. Pedone, Livio & Molaioni, Filippo & Vallati, Andrea & Pampanin, Stefano, 2023. "Energy refurbishment planning of Italian school buildings using data-driven predictive models," Applied Energy, Elsevier, vol. 350(C).
    18. Zhong, Hai & Wang, Jiajun & Jia, Hongjie & Mu, Yunfei & Lv, Shilei, 2019. "Vector field-based support vector regression for building energy consumption prediction," Applied Energy, Elsevier, vol. 242(C), pages 403-414.
    19. Wang, Zeyu & Liu, Jian & Zhang, Yuanxin & Yuan, Hongping & Zhang, Ruixue & Srinivasan, Ravi S., 2021. "Practical issues in implementing machine-learning models for building energy efficiency: Moving beyond obstacles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    20. Li, Xinyi & Yao, Runming, 2020. "A machine-learning-based approach to predict residential annual space heating and cooling loads considering occupant behaviour," Energy, Elsevier, vol. 212(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:gam:jeners:v:18:y:2025:i:18:p:5036-:d:1755072. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.