Introducing the best model for estimation the monthly mean daily global solar radiation on a horizontal surface (Case study: Algeria)

Mecibah, Mohamed-Salah; Boukelia, Taqiy Eddine; Tahtah, Reda; Gairaa, Kacem

doi:10.1016/j.rser.2014.04.054

Cited by 97 publications

(36 citation statements)

References 30 publications

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“…Trnka et al [41] analyzed 7 methods for estimating daily in the Central Europe case study area (lowlands of Austria and the Czech Republic), where the sunshine-based models were found to be the best of all tested models, followed by cloud-based models, precipitation-based models, and temperature-based models. Mecibah et al [42] introduced the best model for predicting the monthly mean daily on a horizontal surface for 6 Algerian cities, and the results obtained in this study confirmed the previous studies, which indicated that the sunshine-based models were generally more accurate to estimate than temperaturebased models. The amount of solar radiation reaching the earth's surface is closely related to sunshine duration.…”

Section: Discussionsupporting

confidence: 86%

Comparative Analysis of Global Solar Radiation Models in Different Regions of China

Zhang

Cui

Feng

et al. 2018

Advances in Meteorology

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Complete and accurate global solar radiation ( ) data at a specific region are crucial for regional climate assessment and crop growth modeling. The objective of this paper was to evaluate the capability of 12 solar radiation models based on meteorological data obtained from 21 meteorological stations in China. The results showed that the estimated and measured daily had statistically significant correlations ( < 0.01) for all the 12 models in 7 subzones of China. The Bahel model showed the best performance for daily estimation among the sunshine-based models, with average 2 of 0. , and average NS of 0.696, respectively. On monthly scale,Ögelman model showed the best performance among the sunshine-based models, with average RE of 5.66%. The BC model showed the best performance among the temperature-based models, with average RE of 8.26%. Generally, the sunshine-based models were more accurate than the temperature-based models. Overall, the Bahel model is recommended to estimate daily ,Ögelman model is recommended to estimate monthly average daily in China when the sunshine duration is available, and the BC model is recommended to estimate both daily and monthly average daily when only temperature data are available.

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Section: Discussionsupporting

confidence: 86%

Comparative Analysis of Global Solar Radiation Models in Different Regions of China

Zhang

Cui

Feng

et al. 2018

Advances in Meteorology

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“…Estimation of monthly mean daily global irradiance on horizontal surface H G [21] H G ¼ 0:57089 þ 0:01028…”

Section: Dni Estimation Proceduresmentioning

confidence: 99%

Management and exploitation of direct normal irradiance resources for concentrating solar collectors: Algeria as a case study

Mecibah

Boukelia

Benyahia³

2014

Int J Energy Environ Eng

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The use of concentrating solar collectors which are used in solar thermal power plant and concentrated photovoltaic systems implies that these systems only work with the direct normal irradiance (DNI). Unfortunately, DNI ground-based measurements are rarely available at the location of interest, this lack of DNI data contrasts with the availability of global and diffuse horizontal irradiance data (GHI, DHI). Nowadays, several spatial databases that estimate DNI have been developed; however, these databases present uncertainty and provide different values of DNI. In this present study, a proposed methodology for estimating the hourly, monthly mean daily, monthly and annual direct normal irradiance DNI in the presence or absence of measured global and diffuse horizontal irradiances. When applying the proposed method for calculation of DNI for three Algerian ground stations (Algiers, Ghardaia, and Tamanrasset), we obtained better performances than those of the five spatial databases' data, especially for the monthly values. The methodology seems to give a very appropriate way for management and exploitation of DNI resources for the design and analysis of concentrating photovoltaic and thermal systems; and can be applicable to other worldwide locations using empirical equations with new correlation coefficients for estimating direct normal irradiance.

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“…Solar radiation sequences can be treated as a time series produced by random processes; therefore, mathematical models can be used to fit the underlying processes and forecast the future values [4,5]. Many modeling methods have been used to describe solar radiation sequences, including regression methods such as linear regression [4], autoregressive model (AR) [6], autoregressive moving average (ARMA) [7], multi-dimensional linear prediction filters [8], least absolute shrinkage and selection operator (LASSO) [9], and nonlinear model approximators such as artificial neural network (ANN) [10], adaptive neuro-fuzzy inference system [11], hidden Markov model [12], fuzzy logic [13] and Angstrom-Prescott equations [14]. Traditionally, these techniques were used independently by researchers to build forecasting models for solar radiation.…”

Section: Introductionmentioning

confidence: 99%

Hourly Solar Radiation Forecasting Using a Volterra-Least Squares Support Vector Machine Model Combined with Signal Decomposition

et al. 2018

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Accurate solar forecasting facilitates the integration of solar generation into the grid by reducing the integration and operational costs associated with solar intermittencies. A novel solar radiation forecasting method was proposed in this paper, which uses two kinds of adaptive single decomposition algorithm, namely, empirical mode decomposition (EMD) and local mean decomposition (LMD), to decompose the strong non-stationary solar radiation sequence into a set of simpler components. The least squares support vector machine (LSSVM) and the Volterra model were employed to build forecasting sub-models for high-frequency components and low-frequency components, respectively, and the sub-forecasting results of each component were superimposed to obtain the final forecast results. The historical solar radiation data collected on Golden (CO, USA), in 2014 were used to evaluate the accuracy of the proposed model and its comparison with that of the ARIMA, the persistent model. The comparison demonstrated that the superior performance of the proposed hybrid method.

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Introducing the best model for estimation the monthly mean daily global solar radiation on a horizontal surface (Case study: Algeria)

Cited by 97 publications

References 30 publications

Comparative Analysis of Global Solar Radiation Models in Different Regions of China

Comparative Analysis of Global Solar Radiation Models in Different Regions of China

Management and exploitation of direct normal irradiance resources for concentrating solar collectors: Algeria as a case study

Hourly Solar Radiation Forecasting Using a Volterra-Least Squares Support Vector Machine Model Combined with Signal Decomposition

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