Machine Learning Models for Regional Photovoltaic Power Generation Forecasting with Limited Plant-Specific Data

Mauro Tucci (), Antonio Piazzi and Dimitri Thomopulos
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Mauro Tucci: Department of Energy, Systems, Territory and Constructions Engineering, University of Pisa, L.go Lucio Lazzarino 1, 56122 Pisa, Italy
Antonio Piazzi: i-EM s.r.l., Via Aurelio Lampredi, 45, 57121 Livorno, Italy
Dimitri Thomopulos: Department of Energy, Systems, Territory and Constructions Engineering, University of Pisa, L.go Lucio Lazzarino 1, 56122 Pisa, Italy

Energies, 2024, vol. 17, issue 10, 1-21

Abstract: Predicting electricity production from renewable energy sources, such as solar photovoltaic installations, is crucial for effective grid management and energy planning in the transition towards a sustainable future. This study proposes machine learning approaches for predicting electricity production from solar photovoltaic installations at a regional level in Italy, not using data on individual installations. Addressing the challenge of diverse data availability between pinpoint meteorological inputs and aggregated power data for entire regions, we propose leveraging meteorological data from the centroid of each Italian province within each region. Particular attention is given to the selection of the best input features, which leads to augmenting the input with 1-hour-lagged meteorological data and previous-hour power data. Several ML approaches were compared and examined, optimizing the hyperparameters through five-fold cross-validation. The hourly predictions encompass a time horizon ranging from 1 to 24 h. Among tested methods, Kernel Ridge Regression and Random Forest Regression emerge as the most effective models for our specific application. We also performed experiments to assess how frequently the models should be retrained and how frequently the hyperparameters should be optimized in order to comprise between accuracy and computational costs. Our results indicate that once trained, the model can provide accurate predictions for extended periods without frequent retraining, highlighting its long-term reliability.

Keywords: renewable energy prediction; solar photovoltaic forecasting; machine learning; regional electricity production prediction (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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