A review on solar radiation assessment and forecasting in Algeria

Kabouche, Nourdine; Chellali, Farouk; Recioui, Abdelmadjid

doi:10.51485/ajss.v6i3.141

Cited by 6 publications

(5 citation statements)

References 26 publications

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“…Linear μ Ai (x)=a.x+b (6) {a.b.c.d} are MFs parameter sets where the maximum and minimum values are 1 and 0 [13].…”

Section: Logical Operation and Input Inputmfmentioning

confidence: 99%

“…Artificial neural networks (ANNs) produce favorable outcomes with limited parameters; therefore, they are frequently used for solar radiation predictions. Using meteorological data from the province of Isparta [6], reference [7] explored the forecasting of solar radiation using Random Forest, Artificial Neural Network, k-Nearest Neighbor, and Deep Learning techniques. The usage of dummy variables, according to the results, increases performance for ANN and DL methods while decreasing it for RF and k-NN methods.…”

Section: Introductionmentioning

confidence: 99%

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Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in South Algeria

Halima,

Djelloul,

Mehdi

et al. 2024

CMC

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When designing solar systems and assessing the effectiveness of their many uses, estimating sun irradiance is a crucial first step. This study examined three approaches (ANN, GA-ANN, and ANFIS) for estimating daily global solar radiation (GSR) in the south of Algeria: Adrar, Ouargla, and Bechar. The proposed hybrid GA-ANN model, based on genetic algorithm-based optimization, was developed to improve the ANN model. The GA-ANN and ANFIS models performed better than the standalone ANN-based model, with GA-ANN being better suited for forecasting in all sites, and it performed the best with the best values in the testing phase of Coefficient of Determination (R = 0.9005), Mean Absolute Percentage Error (MAPE = 8.40%), and Relative Root Mean Square Error (rRMSE = 12.56%). Nevertheless, the ANFIS model outperformed the GA-ANN model in forecasting daily GSR, with the best values of indicators when testing the model being R = 0.9374, MAPE = 7.78%, and rRMSE = 10.54%. Generally, we may conclude that the initial ANN stand-alone model performance when forecasting solar radiation has been improved, and the results obtained after injecting the genetic algorithm into the ANN to optimize its weights were satisfactory. The model can be used to forecast daily GSR in dry climates and other climates and may also be helpful in selecting solar energy system installations and sizes.

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“…Linear μ Ai (x)=a.x+b (6) {a.b.c.d} are MFs parameter sets where the maximum and minimum values are 1 and 0 [13].…”

Section: Logical Operation and Input Inputmfmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in South Algeria

Halima,

Djelloul,

Mehdi

et al. 2024

CMC

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“…(a) Left: direct normal solar irradiation in Algeria 26 and right: daily and monthly average solar irradiation in Ghardaia area showing that the solar irradiation can reach the value of 1200 W/m2 27 – 29 (b) Spatial distribution of concentrated solar light intensity at the focal zone of 0.785 m2 off-axis parabolic concentrator, obtained through ZEMAX © calculation. Here, 9 mm FWHM can be obtained with near-Gaussian profile.…”

Section: Solar Laser Systemmentioning

confidence: 99%

“…As illustrated in Fig. 2, assuming a terrestrial solar irradiance of 1200 W/m2 on clear sunny days in desert regions of Algeria, like Ghardaia area 27 – 30 [Fig. 2(a)], an approximate solar power of 894 W can be reached at the focal zone of the 0.785 m2 effective area of the off-axis parabolic mirror [Fig.…”

Section: Solar Laser Systemmentioning

confidence: 99%

Highly efficient end-side-pumping approach of Nd:YAG solar laser with off-axis mirror concentrator

Boutaka,

Liang,

Bouadjemine

et al. 2024

Opt. Eng.

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“…To date, a number of predictive algorithms have been developed and are frequently used to obtain accurate data, including arti cial intelligence(AI) [2,3,4], which is a set of technologies used to create machines that can mimic human intelligence(Arti cial Neural Network (ANN),Fuzzy logic, Adaptive Neuro fuzzy Inference System (ANFIS), Support Vector Machines (SVM)…etc. ).…”

Section: Introductionmentioning

confidence: 99%

Forecast of Global Solar Radiation on a Horizontal Surface by ANN for the City of Batna Algeria

Yahiaoui

Assas

2022

Preprint

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Solar energy is one of the longest-standing renewable in the world. It is available at no charge and could easily be exploited to reduce dependence on hydrocarbon-based energy. Solar radiation data plays an important role in the design, size, and efficiency of energy systems and renewable. However, these data are not available in all cases, particularly in isolated areas. Therefore, prediction of solar radiation values is often the only practical way to obtain such data. In fact, the measured sequences of radiation values are only available for some locations or regions in each country. Neural networks are classified among the techniques of artificial intelligence, its simulation of human reasoning characterizes it and neural networks have contributed to the development of several areas. In prediction, neural networks are used to solve complex forecasting problems. In this work, fifteen neural models are implemented for the modeling and prediction of global solar radiation over a horizontal surface using neural networks. The meteorological parameters are: duration of sunshine (S), hours of light (S0), total extraterrestrial solar radiation, temperature and humidity. Batna has been selected for this study, with its ten years (1996–2005) meteorological datasets collected from the HelioClim1 (HC1) database. The obtained results show that the news neuronal models are efficient for predicting daily global solar radiation with good measurement precision in this town.

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A review on solar radiation assessment and forecasting in Algeria

Cited by 6 publications

References 26 publications

Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in South Algeria

Solar Radiation Estimation Based on a New Combined Approach of Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in South Algeria

Highly efficient end-side-pumping approach of Nd:YAG solar laser with off-axis mirror concentrator

Forecast of Global Solar Radiation on a Horizontal Surface by ANN for the City of Batna Algeria

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