Assessing the performance of global solar radiation empirical equations in Sokoto, Nigeria using meteorological parameters

Garba, M.B.; Muhammad, Auwal; Musa, Md. Nor; Mohammed, A.G.

doi:10.4314/njt.v37i2.10

Cited by 4 publications

(2 citation statements)

References 5 publications

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“…Equations (7) and (8) were developed empirically in this work. Equation (9) was based on a previous report [10]. The empirically developed equations (7) and (8) are an adaptation of Angstrom-Prescott model given as ( ), where it relates the clearness index ( ) and the ratio of the monthly average daily of temperature range to the monthly average daily of day length ( ), which is the modification of the original Angstrom equation.…”

Section: Methodsmentioning

confidence: 99%

Evaluation and Comparison of Temperature-Based Models for The Prediction of The Monthly Average of Daily Global Solar Radiation for Baghdad City- Iraq

Ajil

2020

eijs

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The solar energy is the major source of power for the future and an important source of renewable energy in Iraq and the world. Suitable climate conditions for solar energy are available in Iraq, especially the high temperature in the summer season which extends for more than six months in the year. Hence, the global solar radiation is abundant with high intensity, which is very essential in applicable models for researchers and solar applications. Therefore, nine first-order regression empirical equations of Angstrom-type correlations were used to estimate the more appropriate global solar radiation model for Baghdad city. Two equations were developed empirically in this work, using the most available and easy to get meteorological data, which is the temperature value in various forms. The results of the comparison between the real and calculated values showed reasonable agreement for most equations (including the development of R2- at 99%) as well as the least values of types of errors RMSE and MBE. As an exception, two equations of models failed in this study because of inability to apply to such climate conditions of Baghdad city.

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Section: Methodsmentioning

confidence: 99%

Evaluation and Comparison of Temperature-Based Models for The Prediction of The Monthly Average of Daily Global Solar Radiation for Baghdad City- Iraq

Ajil

2020

eijs

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“…The authors of Ref. [23] investigated the forecasting of global solar irradiance using humidity level, temperature, and a modified sunshine duration factor. The authors envisioned an ANN-based multilayer perceptron model to predict solar irradiance in two distinct regions.…”

Section: Introductionmentioning

confidence: 99%

Advanced Intelligent Approach for Solar PV Power Forecasting Using Meteorological Parameters for Qassim Region, Saudi Arabia

et al. 2023

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Solar photovoltaic (SPV) power penetration in dispersed generation systems is constantly rising. Due to the elevated SPV penetration causing a lot of problems to power system stability, sustainability, reliable electricity production, and power quality, it is critical to forecast SPV power using climatic parameters. The suggested model is built with meteorological conditions as input parameters, and the effects of such variables on predicted SPV power have been studied. The primary goal of this study is to examine the effectiveness of optimization-based SPV power forecasting models based on meteorological conditions using the novel salp swarm algorithm due to its excellent ability for exploration and exploitation. To forecast SPV power, a recently designed approach that is based on the salp swarm algorithm (SSA) is used. The performance of the suggested optimization model is estimated in terms of statistical parameters which include Root Mean Square Error (RMSE), Mean Square Error (MSE), and Training Time (TT). To test the reliability and validity, the proposed algorithm is compared to grey wolf optimization (GWO) and the Levenberg–Marquardt-based artificial neural network algorithm. The values of RMSE and MSE obtained using the proposed SSA algorithm come out as 1.45% and 2.12% which are lesser when compared with other algorithms. Likewise, the TT for SSA is 12.46 s which is less than that of GWO by 8.15 s. The proposed model outperforms other intelligent techniques in terms of performance and robustness. The suggested method is applicable for load management operations in a microgrid environment. Moreover, the proposed study may serve as a road map for the Saudi government’s Vision 2030.

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A New Approach for Prediction of Solar Radiation with Using Ensemble Learning Algorithm

2019

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Assessing the performance of global solar radiation empirical equations in Sokoto, Nigeria using meteorological parameters

Cited by 4 publications

References 5 publications

Evaluation and Comparison of Temperature-Based Models for The Prediction of The Monthly Average of Daily Global Solar Radiation for Baghdad City- Iraq

Evaluation and Comparison of Temperature-Based Models for The Prediction of The Monthly Average of Daily Global Solar Radiation for Baghdad City- Iraq

Advanced Intelligent Approach for Solar PV Power Forecasting Using Meteorological Parameters for Qassim Region, Saudi Arabia

A New Approach for Prediction of Solar Radiation with Using Ensemble Learning Algorithm

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