PV Production Forecast Using Hybrid Models of Time Series with Machine Learning Methods

Thomas Haupt, Oscar Trull () and Mathias Moog
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Thomas Haupt: Engineering Faculty, Hochschule Ansbach, 91522 Ansbach, Germany
Oscar Trull: Department of Applied Statistics, Operational Research and Quality, Universitat Politècnica de València, 46022 Valencia, Spain
Mathias Moog: Engineering Faculty, Hochschule Ansbach, 91522 Ansbach, Germany

Energies, 2025, vol. 18, issue 11, 1-17

Abstract: Photovoltaic (PV) energy production in Western countries increases yearly. Its production can be carried out in a highly distributed manner, not being necessary to use large concentrations of solar panels. As a result of this situation, electricity production through PV has spread to homes and open-field plans. Production varies substantially depending on the panels’ location and weather conditions. However, the integration of PV systems presents a challenge for both grid planning and operation. Furthermore, the predictability of rooftop-installed PV systems can play an essential role in home energy management systems (HEMS) for optimising local self-consumption and integrating small PV systems in the low-voltage grid. In this article, we show a novel methodology used to predict the electrical energy production of a 48 kWp PV system located at the Campus Feuchtwangen, part of Hochschule Ansbach. This methodology involves hybrid time series techniques that include state space models supported by artificial intelligence tools to produce predictions. The results show an accuracy of around 3% on nRMSE for the prediction, depending on the different system orientations.

Keywords: time series; PV; forecast; machine learning (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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