Forecasting Short-Term Photovoltaic Energy Production to Optimize Self-Consumption in Home Systems Based on Real-World Meteorological Data and Machine Learning

Paweł Kut () and Katarzyna Pietrucha-Urbanik ()
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Paweł Kut: Department of Heat Engineering and Air Conditioning, Rzeszow University of Technology, Al. Powstancow Warszawy 6, 35-959 Rzeszow, Poland
Katarzyna Pietrucha-Urbanik: Department of Water Supply and Sewerage Systems, Faculty of Civil, Environmental Engineering and Architecture, Rzeszow University of Technology, Al. Powstancow Warszawy 6, 35-959 Rzeszow, Poland

Energies, 2025, vol. 18, issue 16, 1-31

Abstract: Given the growing number of residential photovoltaic installations and the challenges of self-consumption, accurate short-term PV production forecasting can become a key tool in supporting energy management. This issue is particularly significant in systems without energy storage, where excess production is fed back into the grid, reducing the profitability of prosumer investments. This paper presents an approach to forecasting short-term energy production in residential photovoltaic installations, based on real meteorological data and the use of machine learning methods. The analysis is based on measurement data from a functioning PV installation and a local weather station. This study compares three models: classical linear regression, Random Forest and the XGBoost algorithm. The method of data preparation, the model training process and the assessment of their effectiveness based on real energy production measurements are presented. This paper also includes a practical calculation example and an analysis of selected days in order to compare the forecast results with the actual production. Of the three models compared, the highest accuracy was achieved for XGBoost, with an MAE = 1.25 kWh, RMSE = 1.93 kWh, and coefficient of determination R 2 = 0.94. Compared to linear regression, this means a 66% reduction in MAE and a 41% reduction in the Random Forest model, confirming the practical usefulness of this method in a real-world environment. The proposed approach can be used in energy management systems in residential buildings, without the need to use energy storage, and can support the development of a more conscious use of energy resources on a local scale.

Keywords: photovoltaic; forecasting; linear regression; random forest; XGBoost; energy optimization (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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