Evaluation of Different Solar Radiation Estimation Methods for Indian Locations

Adamala, Sirisha; Reddy, Y.V. Krishna

doi:10.1007/978-981-13-2038-5_5

Cited by 5 publications

(11 citation statements)

References 12 publications

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“…Solar radiation data are important because they are required in many research fields such as meteorology, agriculture, hydrology, ecology, and environment [1][2][3][4]. Solar radiation data are also an important reference for many applications such as solar power plants, engineering designs, regional crop growth modelling, evapotranspiration estimation, and irrigation system development [1,3,5].…”

Section: Introductionmentioning

confidence: 99%

The Ångström–Prescott Regression Coefficients for Six Climatic Zones in South Africa

et al. 2020

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The South African Weather Service (SAWS) manages an in situ solar irradiance radiometric network of 13 stations and a very dense sunshine recording network, located in all six macroclimate zones of South Africa. A sparsely distributed radiometric network over a landscape with dynamic climate and weather shifts is inadequate for solar energy studies and applications. Therefore, there is a need to develop mathematical models to estimate solar irradiation for a multitude of diverse climates. In this study, the annual regression coefficients, a and b, of the Ångström–Prescott (AP) model, which can be used to estimate global horizontal irradiance (GHI) from observed sunshine hours, were calibrated and validated with observed station data. The AP regression coefficients were calibrated and validated for each of the six macroclimate zones of South Africa using the observation data that span 2013 to 2019. The predictive effectiveness of the calibrated AP model coefficients was evaluated by comparing estimated and observed daily GHI. The maximum annual relative Mean Bias Error (rMBE) was 0.371%, relative Mean Absolute Error (rMAE) was 0.745%, relative Root Mean Square Error (rRMSE) was 0.910%, and the worst-case correlation coefficient (R2) was 0.910. The statistical validation metrics results show that there is a strong correlation and linear relation between observed and estimated GHI values. The AP model coefficients calculated in this study can be used with quantitative confidence in estimating daily GHI data at locations in South Africa where daily observation sunshine duration data are available.

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Section: Introductionmentioning

confidence: 99%

The Ångström–Prescott Regression Coefficients for Six Climatic Zones in South Africa

et al. 2020

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“…Maximum MBE of 0.733 MJm -2 d -1 was less than 1.040 and 0.85 MJm -2 d -1 that De Medeiros et al [4] and Tsung et al [14], respectively, determined and the worst case R 2 of 0.910 was greater than 0.875, 0.74 and 0.8 that Zhang et al [3], Adamala et al [2] and De Medeiros et al [4], respectively, determined. The overall validation results from this study are comparable and even better than what was found in similar studies like [2,3,4,5,14] which concluded that the AP coefficients could be used to estimate GHI with confidence based on those validation results. The data used in the study was collected using secondary standard pyranometers (CMP11), which according to Urraca et al [21] generate high quality records of GHI.…”

Section: Validation Resultsmentioning

confidence: 73%

“…Solar radiation data is important because it is required in many research fields such as meteorology, agriculture, hydrology, ecology and environment [1,2,3,4]. Solar radiation data is also an important reference for many applications such as solar power plants, engineering designs, regional crop growth modelling, evapotranspiration estimation and irrigation system development [1,3,5].…”

Section: Introductionmentioning

confidence: 99%

“…SAWS also manage a very dense sunshine duration recording network over South Africa to the extent that sunshine duration data has been continuously measured for several years [8]. SAWS GHI stations are sparse, according to [1,2,3,4,5,9]. Having dense radiometric networks is a worldwide challenge because of the high costs involved in the installation and maintenance of the solar radiation stations.…”

Section: Introductionmentioning

confidence: 99%

“…The regression coefficients a and b are site dependent, therefore there is a need to calibrate them using a linear relationship in equation ( 1) at regions where they will be used to estimate GHI [1,4,13]. Researchers such as those from the Chinese Academy of Sciences, the Indian National Academy of Agricultural Research Management, the Brazilian Federal University of Rio Grande do Norte and Spanish Polytechnic University of Madrid [1][2][3][4][5] calibrated AP coefficients to their own climatic regions by using the linear relationship in equation (1). According to works by three different research groups [1,2,14] sunshine-based models provided better GHI estimates when compared to cloud and temperature-based models.…”

Section: Introductionmentioning

confidence: 99%

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The Ångström-Prescott Regression Coefficients for Six Climatic Zones in South Africa

Mabasa¹,

Lysko²,

Tazvinga³

et al. 2020

Preprint

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The South African Weather Service (SAWS) manages an in-situ solar irradiance radiometric network of 13 stations and a very dense sunshine recording network; located in all six macro-climate zones of South Africa. A sparsely distributed radiometric network and over a landscape with dynamic climate and weather shifts is inadequate for solar energy studies and applications. Therefore, there is a need to develop mathematical models to estimate solar irradiation for a multitude of diverse climates. In this study, the annual regression coefficients, a and b, of the Ångström-Prescott (AP) model that can be used to estimate global horizontal irradiance from observed sunshine hours were calibrated and validated with observed station data. The AP regression coefficients were calibrated and validated for each of the six macro-climate zones of South Africa using the observation data that spans 2013 to 2019. The predictive effectiveness of the calibrated AP model coefficients was evaluated by comparing estimated and observed daily global horizontal irradiance. The maximum annual relative Mean Bias Error (rMBE) was 0.371 %, relative Mean Absolute Error (rMAE) was 0.745 %, relative Root Mean Square Error (rRMSE) was 0.910 % and the worst-case correlation coefficient (R2) was 0.910. The statistical validation metrics results show that there is a strong correlation and linear relation between observed and estimated solar radiation values. The AP model coefficients calculated in this study can be used with quantitative confidence in estimating daily GHI data at locations in South Africa where the daily observation sunshine duration data is available.

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