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

Daud Mustafa Minhas, Muhammad Usman, Irtaza Bashir Raja, Aneela Wakeel, Muzaffar Ali and Georg Frey ()
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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

Energies, 2025, vol. 18, issue 18, 1-21

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.

Keywords: time-ahead weather input; supervised learning; ensemble prediction models; energy behavior modeling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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