SolNet: Open-source deep learning models for photovoltaic power forecasting across the globe

Joris Depoortere, Johan Driesen, Johan Suykens and Hussain Syed Kazmi

International Journal of Forecasting, 2025, vol. 41, issue 3, 1223-1236

Abstract: Deep learning models have gained increasing prominence in recent years in solar photovoltaic (PV) forecasting. One drawback of these models is that they require a lot of high-quality data to perform well. This is often infeasible in practice, due to poor measurement infrastructure in legacy systems and the rapid build-up of new solar systems across the world. This paper proposes SolNet: a novel, general-purpose, multivariate solar power forecaster, which addresses these challenges by using a two-step forecasting pipeline that incorporates transfer learning from abundant synthetic data generated from PVGIS, before fine-tuning on observational data.

Keywords: PV forecasting; Transfer learning; Synthetic data; Neural networks; Seasonality (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:41:y:2025:i:3:p:1223-1236

DOI: 10.1016/j.ijforecast.2024.12.003

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