Development of a Method For Estimating Solar and Wind Potential Based On Data Analysis

The effective integration of renewable energies into electrical grids requires precise planning of optimal production periods. This study presents a technique for identifying optimal production periods for photovoltaic and wind generators installed at any geographical point. This method is based on meteorological modeling and analysis of historical data. By leveraging past meteorological data, this approach identifies seasonal and daily trends to predict optimal periods of solar and wind energy production. The technique was implemented by applying it to a site located in the territory of Benin. Indeed, irradiation, temperature, and clarity index were identified as parameters influencing photovoltaic production. Wind speed was identified as the main parameter influencing wind production. These influencing parameters were downloaded from the NASA database from January 1, 2013, to December 31, 2023. We then developed characterization criteria to assess the potential for exploiting PV and wind generators in a given area and to determine optimal production periods. These optimal production periods depend not only on the potential of the location where they are installed but also on the temporal evolution of the parameters affecting the output power of PV and wind generators. Through this article, with our method, it is therefore possible to characterize any geographical point for its ability to accommodate PV, wind, PV-wind generators, and determine optimal production seasons.

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Published
2024-10-07
DOI
https://doi.org/10.1109/powerafrica61624.2024.10759490
Citations
1
Primary Topic
Energy Load and Power Forecasting
Type
article
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0.21
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article

Development of a Method For Estimating Solar and Wind Potential Based On Data Analysis

Victor Zogbochi, Patrice Koffi Chetangny, Gérald Barbier, Mèhundo Walix Leslie De et al.
1 citations
Energy Load and Power Forecasting
0.21
article

Development of a Method For Estimating Solar and Wind Potential Based On Data Analysis

Victor Zogbochi, Patrice Koffi Chetangny, Gérald Barbier, Mèhundo Walix Leslie De, Didier Chamagne, Macaire Agbomahéna
article en
1 citations

Abstract

The effective integration of renewable energies into electrical grids requires precise planning of optimal production periods. This study presents a technique for identifying optimal production periods for photovoltaic and wind generators installed at any geographical point. This method is based on meteorological modeling and analysis of historical data. By leveraging past meteorological data, this approach identifies seasonal and daily trends to predict optimal periods of solar and wind energy production. The technique was implemented by applying it to a site located in the territory of Benin. Indeed, irradiation, temperature, and clarity index were identified as parameters influencing photovoltaic production. Wind speed was identified as the main parameter influencing wind production. These influencing parameters were downloaded from the NASA database from January 1, 2013, to December 31, 2023. We then developed characterization criteria to assess the potential for exploiting PV and wind generators in a given area and to determine optimal production periods. These optimal production periods depend not only on the potential of the location where they are installed but also on the temporal evolution of the parameters affecting the output power of PV and wind generators. Through this article, with our method, it is therefore possible to characterize any geographical point for its ability to accommodate PV, wind, PV-wind generators, and determine optimal production seasons.

Centre National de la Recherche Scientifique (FR), Université de Haute-Alsace (FR), Franche-Comté Électronique Mécanique Thermique et Optique - Sciences et Technologies (FR)
Affordable and clean energy
Openalex Percentile: Top 44%
Energy Load and Power Forecasting
0.21
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Development of a Method For Estimating Solar and Wind Potential Based On Data Analysis — Victor Zogbochi, Patrice Koffi Chetangny, et al. · (2024) | TGRS Research Map | TGRS