A Machine Learning Approach and Methodology for Solar Radiation Assessment Using Multispectral Satellite Images

Verma, Preeti; Patil, Sunil

doi:10.1007/s40745-021-00352-x

Cited by 7 publications

(2 citation statements)

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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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Machine Learning for Cloud Cover Detection Using Multispectral Satellite Images

Verma

Patil

2022

Ann. Data. Sci.

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A Machine Learning Approach and Methodology for Solar Radiation Assessment Using Multispectral Satellite Images

Cited by 7 publications

References 50 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

Machine Learning for Cloud Cover Detection Using Multispectral Satellite Images

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