A Collection of Models for the Estimation of Global Solar Radiation in Algeria

Salmi, Mohamed; Chegaar, M.; Mialhe, Pauline

doi:10.1080/15567240903485949

Cited by 10 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…But the proposed sine and cosine wave trigonometric model (#7) produces the best result. On the contrary, the other models (i.e., Mecibah-model [31] and Salmi-model [32]) give a bad result and the worst model is the Salmi-model [32].…”

Section: Statistical Evaluationmentioning

confidence: 98%

“…Hence, three models are chosen in the process of the evaluation. These models used the modified Angstrom-Prescott model [34] to estimate the monthly mean global solar radiation on a horizontal surface for Oran region [30][31][32]: Nia et al [30] evaluated and compared an eight regression models to estimate the monthly mean daily solar radiation on a horizontal surface under the four Algerian climate area based on sunshine duration. It's found that the best and accuracy model concerning the Oran region is the cubic model as present in Eq.…”

Section: Statistical Evaluationmentioning

confidence: 99%

“…Fig. 1, shows a comparison between measured data and estimated monthly average global solar radiation models, i.e., Nia-model [30], Mecibah-model [31], Salmi-model [32], polynomial model (#4) and sine and cosine wave trigonometric model (#7), for Oran, Algeria. It is seen from the figure a remarkable agreement between the measured and estimated values for the polynomial model (#4), Nia-model [30] and sine and cosine wave trigonometric model (#7).…”

Section: Statistical Evaluationmentioning

confidence: 99%

“…The regression models were carried out during the period 2006-2010. The developed models are compared and validated with three existing models proposed in [30][31][32], in order to introduce the best model by using some statistical indicators.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Simple Correlation Models for Estimation of Horizontal Global Solar Radiation for Oran, Northwest Algeria

Aoun

Bouchouicha

2017

JERA

View full text Add to dashboard Cite

In the literature, several correlation models have been developed to predict solar radiation on horizontal surfaces. In this paper, the daily solar radiation data and sunshine duration data measured during the period of 2006-2010, were used to calculate the monthly mean values of daily total solar radiation using five modified Angstrom–Prescott model and three day of the year based empirical models of Oran city (35°42 N and 00°36 W) in Algeria. Furthermore, those modified models are compared with three existing models. In order to evaluate the accuracy of the models, the statistical testing error such as R2, RMSE, rRMSE, MAPE and MBE are used. According to statistical test results and from the sunshine duration models, the polynomial model (#4) showed the best estimation results with a coefficient of determination R2=0.991, rRMSE=4.129%, and MAPE=3.635%. Furthermore, the accurate model from the day of the year models is the model (7), with R2=0.987, rRMSE=4.067%, and MAPE=3.5194%. Moreover, this paper finds that the best accuracy model to estimate the monthly mean daily solar radiation on horizontal surfaces in Oran city is the sine and cosine wave trigonometric model (#7).

show abstract

Section: Statistical Evaluationmentioning

confidence: 98%

Section: Statistical Evaluationmentioning

confidence: 99%

Section: Statistical Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Simple Correlation Models for Estimation of Horizontal Global Solar Radiation for Oran, Northwest Algeria

Aoun

Bouchouicha

2017

JERA

View full text Add to dashboard Cite

show abstract

“…Comprehensive analysis of solar energy potential is required for performance prediction, planning and implementation of solar energy devices 5–7 . Solar radiation data, modeling, and estimating techniques are used to calculate the solar potential of a particular zone 8 .…”

Section: Introductionmentioning

confidence: 99%

Prediction of diffuse solar radiation using machine learning models based on sunshine period and sky‐clearness index for the humid‐subtropical climate of India

Mustafa

Husain

Khan

et al. 2022

Env Prog and Sustain Energy

View full text Add to dashboard Cite

Machine learning (ML) models were developed to estimate monthly average diffuse solar radiation (DSR) with sky-clearness index and relative sunshine period as inputs for the humid subtropical climate of India. Three categories of ML models were defined, each having six models. The solar radiation data was split into two parts, the "Training dataset" used to develop the models and the "Validation dataset" used to test the models. Model accuracy was investigated as a function of various commonly used statistical pointers. A comparison was made between the ML models and wellknown empirical models from prior research. Global performance indicator was used to rank ML models within each category. The projected values from the ML models and solar radiation data were in reasonable agreement. Thus, DSR can be accurately predicted using ML models in the area under consideration.

show abstract

Theoretical prediction and validation of global horizontal solar irradiance for a tropical climate in India

Sundaram

Babu

2015

Front. Energy

View full text Add to dashboard Cite

A Collection of Models for the Estimation of Global Solar Radiation in Algeria

Cited by 10 publications

References 14 publications

Simple Correlation Models for Estimation of Horizontal Global Solar Radiation for Oran, Northwest Algeria

Simple Correlation Models for Estimation of Horizontal Global Solar Radiation for Oran, Northwest Algeria

Prediction of diffuse solar radiation using machine learning models based on sunshine period and sky‐clearness index for the humid‐subtropical climate of India

Theoretical prediction and validation of global horizontal solar irradiance for a tropical climate in India

Contact Info

Product

Resources

About