Machine Learning and Weather Model Combination for PV Production Forecasting

Amedeo Buonanno, Giampaolo Caputo (), Irena Balog, Salvatore Fabozzi, Giovanna Adinolfi, Francesco Pascarella, Gianni Leanza, Giorgio Graditi and Maria Valenti
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Amedeo Buonanno: Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), 00196 Rome, Italy
Giampaolo Caputo: Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), 00196 Rome, Italy
Irena Balog: Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), 00196 Rome, Italy
Salvatore Fabozzi: Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), 00196 Rome, Italy
Giovanna Adinolfi: Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), 00196 Rome, Italy
Francesco Pascarella: Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), 00196 Rome, Italy
Gianni Leanza: Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), 00196 Rome, Italy
Giorgio Graditi: Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), 00196 Rome, Italy
Maria Valenti: Italian National Agency for New Technologies, Energy and Sustainable Economic Development (ENEA), 00196 Rome, Italy

Energies, 2024, vol. 17, issue 9, 1-15

Abstract: Accurate predictions of photovoltaic generation are essential for effectively managing power system resources, particularly in the face of high variability in solar radiation. This is especially crucial in microgrids and grids, where the proper operation of generation, load, and storage resources is necessary to avoid grid imbalance conditions. Therefore, the availability of reliable prediction models is of utmost importance. Authors address this issue investigating the potential benefits of a machine learning approach in combination with photovoltaic power forecasts generated using weather models. Several machine learning methods have been tested for the combined approach (linear model, Long Short-Term Memory, eXtreme Gradient Boosting, and the Light Gradient Boosting Machine). Among them, the linear models were demonstrated to be the most effective with at least an RMSE improvement of 3.7% in photovoltaic production forecasting, with respect to two numerical weather prediction based baseline methods. The conducted analysis shows how machine learning models can be used to refine the prediction of an already established PV generation forecast model and highlights the efficacy of linear models, even in a low-data regime as in the case of recently established plants.

Keywords: machine learning; PV forecasting; microgrids; signal processing (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: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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