Comparison And Validation Of Regression Model For Estimation Of Global Solar Radiation Using Python

Kumar, M. Praveen; Kumar, Prabhat; Mukherjee, Soumyadeep; Priyadarshini, Arunjyoti; Biswas, Samyak; Namrata, Kumari

doi:10.1109/sceecs54111.2022.9740775

Cited by 5 publications

(2 citation statements)

References 6 publications

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“…The solar declination angle, δ, and the sunset hour angle, ω s , are computed using Eqs. ( 3) and (4), respectively [79].…”

Section: Meteorological Parametersmentioning

confidence: 99%

Angstrom-Prescott Type Models for Predicting Solar Irradiation for Different Locations in Zimbabwe

Iradukunda¹,

Chiteka²

2023

sv-jme

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Adequate assessment of solar radiation data is crucial for planning and designing solar energy systems. However, a major challenge facing solar energy technologies is the availability of solar radiation data at the specific area of interest. In this paper solar radiation and sunshine duration data from 29 stations in Zimbabwe were used to generate both monthly and annual Angstrom-Prescott (A-P) type coefficients, a and b, that are location based. The coefficients were developed using linear correlation between the clearness index and sunshine duration. The adaptation relationship between satellite and ground-measured irradiation had an R2 of 0.6738. The correlation between the clearness index and the sunshine duration in most of the stations was fairly high with the highest coefficient of determination, R2, of 0.9030. The A-Pregression coefficient, a, generated using the data from each station ranged between 0.2252 and 0.3976, whereas the regression coefficient, b, ranged between 0.3218 and 0.6265. The estimated and measured values of global solar radiation, He, and Hm, respectively from each station were compared using the mean absolute percentage error (MAPE), the root mean square error (RMSE), the mean absolute error (MAE) and the relative standard error (RSE). The MAE values for the models ranged from 0.5438 MJ/m2 to 2.2845 MJ/m2. The MAPE indicated a range between 2.5642 % and 10.334 %. The RSE ranged between 0.0346 % and 0.1537% while the RMSE for the models ranged from 0.7360 MJ/m2 to 2.9454 MJ/m2. The statistical indicators showed results that were within the recommended range for solar radiation predicting models from similar studies.

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“…The solar declination angle, δ, and the sunset hour angle, ω s , are computed using Eqs. ( 3) and (4), respectively [79].…”

Section: Meteorological Parametersmentioning

confidence: 99%

Angstrom-Prescott Type Models for Predicting Solar Irradiation for Different Locations in Zimbabwe

Iradukunda¹,

Chiteka²

2023

sv-jme

View full text Add to dashboard Cite

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“…The smaller value obtained in the test result means that the model performs better. The results of the RMSE and MBE tests are also used in this test method, t-stat test is obtained as follows 47,49. T A B L E 6 The statistical test results models for Region 2.…”

mentioning

confidence: 99%

Using of the Weibull distribution in developing global solar radiation forecasting models

Kaplan,

Kaplan

2024

Env Prog and Sustain Energy

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In this study, a solar radiation prediction model was developed using the Weibull distribution function (WDF). The main purpose of this study is to develop a new model for solar radiation (SR) estimation using the WDF and to bring this model to the literature. Although the WDF is widely used in wind energy forecasting due to its compatibility with wind speed data, it has not been used before in solar radiation forecasting model development. In this study, it is aimed to make SR estimation with the WDF for the first time. SR data for all regions where this new model was developed and its performance was examined were obtained from the General Directorate of Meteorology. In order to decide the success of these developed models, five different statistical metrics (RPE, MPE, MAPE, SSRE, and t‐stat) were discussed in the study. When the results are evaluated in general, it is seen that the new developed model has an acceptable performance. According to all test results in the region where the model was developed, it is seen that the new developed model showed the best performance with 0.0353, 0.4068, 7.1851, 0.1144, and 0.0162 values, respectively. The performance of this new developed model was observed in three different regions. When the statistical test results in these regions were examined, it was seen that the new model developed had a similar performance to the popular solar forecasting models widely used in the literature.

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Solar radiation estimation for photovoltaic power generation using Geostationary and Low-Earth Orbit Satellite Data

Tobishima,

Urano

2023

2023 IEEE Belgrade PowerTech

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Comparison And Validation Of Regression Model For Estimation Of Global Solar Radiation Using Python

Cited by 5 publications

References 6 publications

Angstrom-Prescott Type Models for Predicting Solar Irradiation for Different Locations in Zimbabwe

Angstrom-Prescott Type Models for Predicting Solar Irradiation for Different Locations in Zimbabwe

Using of the Weibull distribution in developing global solar radiation forecasting models

Solar radiation estimation for photovoltaic power generation using Geostationary and Low-Earth Orbit Satellite Data

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