Título:
A computational intelligence approach for solar photovoltaic power generation forecasting
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Autor:
Nesmachnow, Sergio
Risso, Claudio
Risso, Claudio
Tutor:
Tipo de documento:
Artículo
Editor:
Palabras clave:
Solar photovoltaic
Forecasting
Neural networks
Computational intelligence
Forecasting
Neural networks
Computational intelligence
Descriptores:
Año de publicación:
2024
Contenido:
Resumen:
This article describes an approach applying computational intelligence methods for the problem of forecasting solar photovoltaic power generation at country level. Precise forecast of power generation plays a vital role in designing a dependable photovoltaic power generation system. The computed predictions enable the implementation of efficient planning, management, and distribution strategies for the generated power, ultimately enhancing the performance and efficiency of the system. The study analyzes and compares artificial neural network approaches for a specific case study using real solar photovoltaic power generation data from Uruguay in the period 2018 to 2022. Several artificial neural network architectures are evaluated for forecasting. The main results indicate that the approach applying a combination of Encoder-Decoder and Long Short Term Memory artificial neural networks is the most effective method for the addressed forecasting problem. The approach yielded promising results, with an average mean error value of 0.09, improving over the other artificial neural network architectures. Even better results were obtained for sunny days. The generated forecasts hold significant value for its application in planning and scheduling processes, aiming to enhance the overall quality of service of the electricity grid.
Descripción:
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Editorial:
Sage
EN:
Renewable Energies, vol. 2, no. 1, jan. 2024, pp. 1-18, DOI: 10.1177/27533735241237990.
Financiadores:
La investigación presentada en este artículo se desarrolló como parte de un proyecto conjunto entre la UTE y la Universidad de la República, Uruguay.
Citación:
Nesmachnow, S. y Risso, C. "A computational intelligence approach for solar photovoltaic power generation forecasting". Renewable Energies. [en línea]. 2024, vol. 2, no. 1, pp. 1-18. DOI: 10.1177/27533735241237990.
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ISSN:
2753-3735
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Licencia:
Licencia Creative Commons Atribución - No Comercial (CC - By-NC 4.0)
Colecciones:
| Ficheros | Descripción | Tamaño | Formato | ||
|---|---|---|---|---|---|
| NR24.pdf | Versión publicada | 4.77 MB | Adobe PDF |