Forecasting Heat Power Demand in Retrofitted Residential Buildings

Łukasz Guz (), Dariusz Gaweł, Tomasz Cholewa, Alicja Siuta-Olcha, Martyna Bocian and Mariia Liubarska
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Łukasz Guz: Faculty of Environmental Engineering, Lublin University of Technology, 20-618 Lublin, Poland
Dariusz Gaweł: Faculty of Civil Engineering and Architecture, Lublin University of Technology, 20-618 Lublin, Poland
Tomasz Cholewa: Faculty of Environmental Engineering, Lublin University of Technology, 20-618 Lublin, Poland
Alicja Siuta-Olcha: Faculty of Environmental Engineering, Lublin University of Technology, 20-618 Lublin, Poland
Martyna Bocian: Faculty of Environmental Engineering, Lublin University of Technology, 20-618 Lublin, Poland
Mariia Liubarska: Faculty of Environmental Engineering, Lublin University of Technology, 20-618 Lublin, Poland

Energies, 2025, vol. 18, issue 3, 1-26

Abstract: The accurate prediction of heat demand in retrofitted residential buildings is crucial for optimizing energy consumption, minimizing unnecessary losses, and ensuring the efficient operation of heating systems, thereby contributing to significant energy savings and sustainability. Within the framework of this article, the dependence of the energy consumption of a thermo-modernized building on a chosen set of climatic factors has been meticulously analyzed. Polynomial fitting functions were derived to describe these dependencies. Subsequent analyses focused on predicting heating demand using artificial neural networks (ANN) were adopted by incorporating a comprehensive set of climatic data such as outdoor temperature; humidity and enthalpy of outdoor air; wind speed, gusts, and direction; direct, diffuse, and total radiation; the amount of precipitation, the height of the boundary layer, and weather forecasts up to 6 h ahead. Two types of networks were analyzed: with and without temperature forecast. The study highlights the strong influence of outdoor air temperature and enthalpy on heating energy demand, effectively modeled by third-degree polynomial functions with R 2 values of 0.7443 and 0.6711. Insolation (0–800 W/m 2 ) and wind speeds (0–40 km/h) significantly impact energy demand, while wind direction is statistically insignificant. ANN demonstrates high accuracy in predicting heat demand for retrofitted buildings, with R 2 values of 0.8967 (without temperature forecasts) and 0.8968 (with forecasts), indicating minimal performance gain from the forecasted data. Sensitivity analysis reveals outdoor temperature, solar radiation, and enthalpy of outdoor air as critical inputs.

Keywords: heat power demand; foresting; retrofit; residential buildings; ANN (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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