Characterizing solar radiation zones in the Canary Islands using cluster analysis

• New methodology applied for characterizing radiation in a territory with significant orographic variation. • Classification and harmonization of data from meteorological stations to obtain different radiation zones. • Four radiation zones have been identified on the Canary Island Archipelago. • Average daily irradiation is inadequate for accurately representing each zone. This study categorizes solar radiation patterns across the Canary Islands using the clearness index (Kt) and K-means clustering. We have used a comprehensive dataset compiled from multiple meteorological sources to identify distinct radiation zones. The K-means algorithm, applied to the clearness index data, revealed four unique radiation zones within the archipelago. To validate these clusters, we compared them against observed radiation data using several performance metrics: Mean Absolute Bias Error (MABE), Mean Absolute Percentage Error (MAPE), Mean Bias Error (MBE), Root Mean Square Error (RMSE), and relative RMSE (rRMSE), resulting four zones. Each of the four identified zones is characterized by varying frequencies of cloudy, partially clear, and very clear days. Zone 1 exhibits the lowest radiation, defined by a high prevalence of cloudy days, while Zone 4 shows the highest radiation due to a significant proportion of clear days. Our findings emphasize the need for tailored calibration to improve predictive accuracy within each specific zone. Although high prediction errors were observed, this clustering approach effectively categorizes solar radiation patterns in the Canary Islands, suggesting that further model refinement could significantly enhance the accuracy of solar radiation forecasts.

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Publication Details

Journal
Results in Engineering
Published
2025-10-17
DOI
https://doi.org/10.1016/j.rineng.2025.107795
Primary Topic
Solar Radiation and Photovoltaics
Type
article
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article

Characterizing solar radiation zones in the Canary Islands using cluster analysis

Ana Pérez-García, E. Jaizme-Vega, Eduardo González-Díaz, Norena Martín Dorta et al.
Results in Engineering
Solar Radiation and Photovoltaics
article

Characterizing solar radiation zones in the Canary Islands using cluster analysis

Ana Pérez-García, E. Jaizme-Vega, Eduardo González-Díaz, Norena Martín Dorta, José Miguel Márquez-Martinón, Benjamín González‐Díaz, Y. Fernández-Ogando, R. Fernández-Vega, J. Jubera-Pérez
article en

Abstract

• New methodology applied for characterizing radiation in a territory with significant orographic variation. • Classification and harmonization of data from meteorological stations to obtain different radiation zones. • Four radiation zones have been identified on the Canary Island Archipelago. • Average daily irradiation is inadequate for accurately representing each zone. This study categorizes solar radiation patterns across the Canary Islands using the clearness index (Kt) and K-means clustering. We have used a comprehensive dataset compiled from multiple meteorological sources to identify distinct radiation zones. The K-means algorithm, applied to the clearness index data, revealed four unique radiation zones within the archipelago. To validate these clusters, we compared them against observed radiation data using several performance metrics: Mean Absolute Bias Error (MABE), Mean Absolute Percentage Error (MAPE), Mean Bias Error (MBE), Root Mean Square Error (RMSE), and relative RMSE (rRMSE), resulting four zones. Each of the four identified zones is characterized by varying frequencies of cloudy, partially clear, and very clear days. Zone 1 exhibits the lowest radiation, defined by a high prevalence of cloudy days, while Zone 4 shows the highest radiation due to a significant proportion of clear days. Our findings emphasize the need for tailored calibration to improve predictive accuracy within each specific zone. Although high prediction errors were observed, this clustering approach effectively categorizes solar radiation patterns in the Canary Islands, suggesting that further model refinement could significantly enhance the accuracy of solar radiation forecasts.

Results in EngineeringVol. 28
Universidad de La Laguna (ES), Gobierno de Canarias (ES)
Openalex Percentile: Top 84%
Solar Radiation and Photovoltaics
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