Modelling the Earth’s Solar Irradiance Across Some Selected Stations in Sub-Sahara Region of Africa

Aweda, F. O.; Samson, Timothy Kayode

doi:10.5829/ijee.2020.11.03.05

Cited by 4 publications

(3 citation statements)

References 5 publications

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“…In commaseparated value (CSV) data format, data for thirty-four (34) years covering 1985 to 2019 were retrieved as monthly averages for January to December of each year. The data was collected using the method described in literature [25][26][27][28]. Environmental running of statistical packages (i.e The Mann-Kendal statistics was used to analyze the trend in rainfall series while the normality of rainfall series was determined using the Kolmogorov-Smirnov test) was done for data plotting and curve fittings.…”

Section: Methodsmentioning

confidence: 99%

Relationship between Air Temperature and Rainfall Variability of Selected Stations in Sub-Sahara Africa

Aweda

Samson

2022

IJEE

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This current study was conducted on rainfall and air temperature data obtained from the archive of the HelioClim website to determine the relationship between the two parameters. The study aimed at the relationship between rainfall and air temperature. The data of thirty-four (34) years spanning from 1985 to 2019 was analyzed using Mann-Kendal statistics on the trend of the rainfall series while the normality of rainfall series was determined using Kolmogorov-Smirnov test across six southwest stations of Nigeria. The results revealed the highest mean rainfall in Akure (198.9 mm) while the least rainfall in Ado-Ekiti (163.4 mm). The maximum rainfall was in Abeokuta (865.8 mm) with Iwo having the highest disparity in rainfall (SD=148.8 mm) compared with other stations. The skewness in Abeokuta (Skewness = 0.9 mm) was higher compared with Ado-Ekiti, Akure, Ibadan, Ikeja and Iwo with skewness values of 0.7 mm, 0.4 mm, 0.7 mm, 0.6 mm and 0.7 mm, respectively. The maximum air temperature was recorded in Iwo (301.7 K) and the minimum air temperature in Ado-Ekiti (293.3 K). The skewness obtained in Akure (-0.2) and Ikeja (-0.3) was less than zero indicating that air temperature decreased more than it increased in these areas while in other stations, Abeokuta (0.01), Ado-Ekiti (0.22), Ibadan (0.02) and Iwo (0.24), the skewness was greater than zero meaning that air temperature increased more than it decreased in these stations.

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Section: Methodsmentioning

confidence: 99%

Relationship between Air Temperature and Rainfall Variability of Selected Stations in Sub-Sahara Africa

Aweda

Samson

2022

IJEE

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“…The data used in this study consist of monthly rainfall, air temperature, relative humidity, atmospheric pressure, wind speed and direction for five stations obtained from the archive of the HelioClim website of Soda (http://www.soda-pro.com) of MERRA-2 meteorological Re-Analysis data (Gelaro et al 2017, Aweda et al 2020a, Aweda et al 2020b, Aweda et al 2021a, Adebayo et al 2022. The data of forty-one years spanning from 1980 to 2020 were obtained as a monthly average from January to December of every year in Comma Separated Value (CSV) data format.…”

Section: Data Collection and Preparationmentioning

confidence: 99%

Meteorological data prediction over selected stations in Sub-Sahara Africa: Leveraging on Machine Learning Algorithm

Adebayo,

Aweda,

Ojedokun

et al. 2022

Ruhuna J. Sci.

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This study investigated selected meteorological data prediction leveraging on a Machine Learning Algorithm Approach over five selected stations in Nigeria. The algorithm of Machine Learning was explored using weather parameters such as temperature, wind speed, wind direction and relative humidity to predict the rainfall rate. In the results, five Gaussian models (i.e., Rational Quadratic, Squared Exponential, Matern 5/2, Exponential and Optimized GPR) revealed different Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE) with prediction speeds ranging from 15000 to 26000 and the training time included 7.936, 1.8923, 2.3701, 3.267 and 282.19, respectively. The predicted response as against the true response for the two models shows a linear graph passing through the origin which confirmed a perfect regression model, where all the points lie on a diagonal line. Therefore, the relationship between MSE, MAE and RMSE for different models revealed that the optimized GPR has a better performance as compared to others. More so, visualizing the relationship between the output variable (rainfall) and each input variable reveals that some input variables (relative humidity, rainfall, pressure, wind speed and direction) have a strong correlation with the output variable (rainfall), with others having a noisy relationship which is not very clear.

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“…The estimated and measured results were compared using statistical parameter tests such as the mean bias error (MBE), mean percentage error (MPE), mean absolute error (MAE), RMSE, and coefficient of determination (R²). In some work the researchers [20] used ARIMA models to simulate solar irradiance in nine Nigerian locations. Findings revealed that in Kebbi, Iwo, and Maiduguri, the best forecasting ARIMA was (3,0,3), in Calabar as well as Sokoto, it was ARIMA (2,0,2), and in other study areas like Enugu, Ikeja, Ilorin, and Port Harcourt, ARIMA (2,0,3) was the better model for forecasting irradiance.…”

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confidence: 99%

Theoretical Study of Global Solar Radiation on Horizontal Area for Determination of Direct and Diffuse Solar Radiation

Chabane

Moummi

Toumi

et al. 2023

IJEE

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Modelling the Earth’s Solar Irradiance Across Some Selected Stations in Sub-Sahara Region of Africa

Abstract: Solar radiation is an important parameter in the study of electricity and/or thermal system installation. Direct monthly irradiance data measurements of the earth's horizontal surface irradiance for the year 1985 to 2019 for nine stations (Sokoto,

Cited by 4 publications

References 5 publications

Relationship between Air Temperature and Rainfall Variability of Selected Stations in Sub-Sahara Africa

Relationship between Air Temperature and Rainfall Variability of Selected Stations in Sub-Sahara Africa

Meteorological data prediction over selected stations in Sub-Sahara Africa: Leveraging on Machine Learning Algorithm

Theoretical Study of Global Solar Radiation on Horizontal Area for Determination of Direct and Diffuse Solar Radiation

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