Solar Energy Resource Assessment Using GHI and DNI Satellite Data for Moroccan Climate

Alani, Omaima El; Ghennioui, Hicham; Abraim, Mounir; Ghennioui, Abdellatif; Blanc, Philippe; Saint‐Drenan, Yves‐Marie; Naimi, Zakaria

doi:10.1007/978-3-030-94188-8_26

Cited by 5 publications

(3 citation statements)

References 25 publications

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“…This method is particularly useful in converting satellite cloud index data to solar irradiance values, which is essential for solar radiation forecasting and energy applications. Case studies in which the Heliosat method has been used include short-term forecasting of solar radiation [11,16,17], solar energy assessment using remote sensing technologies [18,19], and the deriving of shortwave solar radiation from satellite images [11,20]. The advantages of the Heliosat method include its ability to derive cloud transmission values from satellite data, its adaptability to different satellite sensors, and its capability to provide estimates of solar irradiance based on cloud cover information, contributing to improved solar energy forecasting and resource assessment.…”

Section: Introductionmentioning

confidence: 99%

Validating Meteosat Second Generation and Himawari-8 Derived Solar Irradiance against Ground Measurements: Solarad AI’s Approach

Meher,

Rizvi,

Choudhary

et al. 2024

Energies

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This study assesses the efficacy of the Heliosat-2 algorithm for estimating solar radiation, comparing its outputs against ground measurements across seven distinct countries: the Netherlands, Spain, Japan, Namibia, South Africa, Saudi Arabia, and India. To achieve this, the study utilizes two distinct satellite data sources—Himawari-8 for Japan and Metosat Second Generation-MSG for the rest of the countries—and spanning the time between January 2022 and April 2024. A robust methodology for determining albedo parameters specific to Heliosat-2 was developed. During cloudy days, the estimates provided by Heliosat-2 generally exceeded the ground measurements in all of the countries. Conversely, on clear days, there was a tendency for underestimation, as indicated by the median values of the mean bias (MB) across most of the countries. The Heliosat-2 model slightly underestimates daily radiation values, with a median MB ranging from −27.5 to +10.2 W·m−2. Notably, the median root mean square error (RMSE) on clear days is significantly lower, with values ranging from 24.8 to 108.7 W·m−2, compared to cloudy days, for which RMSE values lie between 75.3 and 180.2 W·m−2. In terms of R2 values, both satellites show strong correlations between the estimated and actual values, with a median value consistently above 0.86 on a monthly scale and over 92% of daily data points falling within ±2 standard deviations.

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Section: Introductionmentioning

confidence: 99%

Validating Meteosat Second Generation and Himawari-8 Derived Solar Irradiance against Ground Measurements: Solarad AI’s Approach

Meher,

Rizvi,

Choudhary

et al. 2024

Energies

View full text Add to dashboard Cite

show abstract

“…This method is particularly useful in converting satellite cloud index data to solar irradiance values, essential for solar radiation forecasting and energy applications. Case studies where the Heliosat method has been used include short-term forecasting of solar radiation [11,16,17], solar energy assessment using remote sensing technologies [18,19], and deriving shortwave solar radiation from satellite images [11,20]. The advantages of the Heliosat method include its ability to derive cloud transmission values from satellite data, its adaptability to different satellite sensors, and its capability to provide estimates of solar irradiance based on cloud cover information, contributing to improved solar energy forecasting and resource assessment.…”

Section: Introductionmentioning

confidence: 99%

Validation of Ground Based Solar Irradiance Measurements against Meteosat-Msg and Himawari-8 Satellite Data

Meher,

Rizvi,

Choudhary

et al. 2024

Preprint

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The study conducts a comparative analysis of solar radiation estimation methods, assessing the efficacy of the Heliosat-2 algorithm against ground measurements across seven distinct countries: Netherlands, Spain, Japan, Namibia, South Africa, Saudi Arabia, and India. To achieve this, it utilizes two distinct satellite data sources—Himawari-8 for Japan and Metosat Second Generation-MSG for the rest of the countries—spanning the years Jan 2022 to April 2024. A robust methodology for determining albedo parameters specific to Heliosat-2 was developed. During cloudy days, the estimates provided by Heliosat-2 generally exceeded the ground measurements in all the countries. Conversely, on clear days, there was a tendency for underestimation, as indicated by the median value of the mean bias (MB) across most of the countries. The Heliosat-2 model slightly underestimates daily radiation values, with a median MB ranging from −27.5 to +10.2 W.m⁻². Notably, the median root mean squared error (RMSE) on clear days is significantly lower, with values ranging from 24.8 to 108.7 W.m⁻², compared to cloudy days where RMSE values lie between 75.3 and 180.2 W.m⁻². In terms of R² values, both satellites show a strong correlation between the estimated and actual values, with a median value consistently above 0.86 on a monthly scale and over 92% of daily data points falling within ±2 standard deviation.

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“…A lot of studies were conducted on ground-based data and satellite-based data [24][25][26][27][28][29]. Some of studies done in the past showed that there were various errors associated with satellite-based models and that is why there was a difference between solar data obtained from satellite models and ground measurements [30][31][32].…”

Section: Introductionmentioning

confidence: 99%

Comparison of Ground-Based Global Horizontal Irradiance and Direct Normal Irradiance with Satellite-Based SUNY Model

et al. 2022

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Since the fossil reserves are depleting day by day, the trend of modern energy sector is going towards renewable energy. The demand of solar power plants is therefore at the peak nowadays across the globe. However, the construction of these plants is extremely dependent on feasibility study to estimate the real solar potential before installing it in any region. To evaluate the solar energy potential of Peshawar region in Pakistan, Ground-based global horizontal irradiance (GHI) and direct normal irradiance (DNI) were compared with satellite-based model SUNY. Ground measurements were done at the University of Engineering and Technology Peshawar (UET Peshawar) with the help of pyranometer and shadowband irradiometer. Comparison of the data showed that there was a maximum difference of 42.90% in ground and satellite-based GHI in the month of December. Minimum difference in GHI was found for the month of March that was −3.83%. Moreover, ground-based GHI was overestimated in the month of February, March, and April, while in rest of the months, satellite values of GHI exceeded the ground measurements. Similarly, maximum difference of 55.86% was found in the month of November between ground and satellite-based DNI while minimum difference of −3.34% was seen in DNI in the month of March between the two data. Furthermore, satellite-based DNI was underestimated in the months of February, March, and April while in rest of the months it was overestimated compared to ground measurements. In addition to this, correlation of ground and satellite-based GHI and DNI showed R2 value of 0.8852 and 0.4139, respectively. The results of this study revealed that the difference between ground measurements and satellite values was considerable and hence real time measurements are necessary to properly estimate solar energy resource in the country.

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Solar Energy Resource Assessment Using GHI and DNI Satellite Data for Moroccan Climate

Cited by 5 publications

References 25 publications

Validating Meteosat Second Generation and Himawari-8 Derived Solar Irradiance against Ground Measurements: Solarad AI’s Approach

Validating Meteosat Second Generation and Himawari-8 Derived Solar Irradiance against Ground Measurements: Solarad AI’s Approach

Validation of Ground Based Solar Irradiance Measurements against Meteosat-Msg and Himawari-8 Satellite Data

Comparison of Ground-Based Global Horizontal Irradiance and Direct Normal Irradiance with Satellite-Based SUNY Model

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