Forecasting photovoltaic production with neural networks and weather features

Stéphane Goutte, Klemens Klotzner, Hoang Viet Le and Hans Jörg von Mettenheim
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Hoang Viet Le: SOURCE - SOUtenabilité et RésilienCE - UVSQ - Université de Versailles Saint-Quentin-en-Yvelines - IRD [Ile-de-France] - Institut de Recherche pour le Développement
Hans Jörg von Mettenheim: IPAG Business School

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Abstract: In this paper, we address the refinement of solar energy forecasting within a 2-day window by integrating weather forecast data and strategically employing entity embedding, with a specific focus on the Multilayer Perceptron (MLP) algorithm. Through the analysis of two years of hourly solar energy production data from 16 power plants in Northern Italy (2020-2021), our research underscores the substantial impact of weather variables on solar energy production. Notably, we explore the augmentation of forecasting models by incorporating entity embedding, with a particular emphasis on embedding techniques for both general weather descriptors and individual power plants. By highlighting the nuanced integration of entity embedding within the MLP algorithm, our study reveals a significant enhancement in forecasting accuracy compared to popular machine learning algorithms like XGBoost and LGBM, showcasing the potential of this approach for more precise solar energy forecasts.

Keywords: Entity embedding; Machine learning; Neural networks; Solar energy; Time series forecasting (search for similar items in EconPapers)
Date: 2024-09-06
New Economics Papers: this item is included in nep-big, nep-cmp, nep-ene, nep-env and nep-for
Note: View the original document on HAL open archive server: https://hal.science/hal-04779953v1
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Published in Energy Economics, 2024, 139, ⟨10.1016/j.eneco.2024.107884⟩

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Journal Article: Forecasting photovoltaic production with neural networks and weather features (2024)
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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04779953

DOI: 10.1016/j.eneco.2024.107884

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