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Communication dans un congrès

Short-term Multi Horizons Forecasting of Solar Irradiation Based on Artificial Neural Network with Meteorological Data: Application in the North-west of Senegal

Abstract : This study proposes a short term forecasting of solar irradiation with multi horizons in the northwest of Senegal. The multilayer artificial neural network (ANN), based on the Levenberg Marquardt algorithm and the meteorological data are used. The latter are measured in real time on the study site. The variables of interest are: mean solar irradiation, maximum temperature and measurement time; they are selected using Weka software. The forecasting horizons are: 0.5 hour, 1 hour, 1.5 hours, 2 hours, 2.5 hours, 3 hours, 3.5 hours, 4 hours, 4.5 hours, 5 hours, 5.5 hours and 06 hours. They are proposed with the corresponding statistical criteria. The results show that, the solar energy forecasting can be extended over a six-hour horizon with a correlation coefficient of 0.97 and root mean square error of 0.07. These results will make it possible to complete the forecasting tools in the solar energy sector in Senegal, and help investors to choose the most suitable horizons for energy forecasting in photovoltaic solar power plants.
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Communication dans un congrès
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https://hal.archives-ouvertes.fr/hal-03479555
Contributeur : Willy Magloire Nkounga Connectez-vous pour contacter le contributeur
Soumis le : mardi 14 décembre 2021 - 13:24:43
Dernière modification le : mercredi 23 mars 2022 - 15:16:01
Archivage à long terme le : : mardi 15 mars 2022 - 19:01:23

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EVER21-6.pdf
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Willy Magloire Nkounga, Mouhamadou Falilou Ndiaye, Oumar Cisse, Momadou Bop, Françoise Grandvaux, et al.. Short-term Multi Horizons Forecasting of Solar Irradiation Based on Artificial Neural Network with Meteorological Data: Application in the North-west of Senegal. 2021 Sixteenth International Conference on Ecological Vehicles and Renewable Energies (EVER), May 2021, Monte-Carlo, France. pp.1-8, ⟨10.1109/EVER52347.2021.9456600⟩. ⟨hal-03479555⟩

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