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The share of photovoltaic energy is more and more increasing in the World energy mix; the intermittence of this production makes difficult to maintain the stability of the electricity grid and the balance production-consumption. Predicting in advance the solar production facilitates the operation of the grid manager. This paper aims to forecast hourly global solar irradiation for time horizons from h+1 to h+6, using two approaches: multiple linear regression (MLR) and artificial neural network (ANN) models. The choice of inputs in these models is crucial for a good prediction and is investigated here; generally, only endogenous data are used (global solar irradiation), the addition of exogenous ones often improves the accuracy (ambient temperature, humidity, pressure and differential pressure) but they are not always available; the introduction of ordinal variables is studied: four ordinal variables allow to introduce the double seasonality of solar irradiation. The performances of two forecasting models (linear and nonlinear models) with combinations of endogenous, exogenous and ordinal variables are compared on two Algerian sites with different meteorological variabilities. It appears that adding ordinal variables to endogenous data decreases the nRMSE values and enables to reach the same level of reliability than adding exogenous variables while simplifying the implementation. This addition as inputs in ANN models decreased nRMSE by 0.45–1.65% points (2.6–6.2%) for Algiers and by 0.2–0.3% point (1–3%) for Ghardaïa according to the forecasting horizon. [ABSTRACT FROM AUTHOR]

This article proposes a new hybrid least squares-support vector machine and artificial bee colony algorithm (ABC-LS-SVM) for multi-hour ahead forecasting of global solar radiation (GHI) data. The framework performs on training the least squares-support vector machine (LS-SVM) model by means of the ABC algorithm using the measured data. ABC is developed for free parameters optimization for the LS-SVM model in a search space so as to boost the forecasting performance. The developed ABC-LS-SVM approach is verified on an hourly scale on a database of five years of measurements. The measured data were collected from 2013 to 2017 at the Applied Research Unit for Renewable Energy (URAER) in Ghardaia, south of Algeria. Several combinations of input data have been tested to model the desired output. Forecasting results of 12 h ahead GHI with the ABC-LS-SVM model led to the root-mean-square error (RMSE) equal to 116.22 Wh/m², Correlation coefficient r = 94.3%. With the classical LS-SVM, the RMSE error equals to 117.73 Wh/m² and correlation coefficient r = 92.42%; for cuckoo search algorithm combined with LS-SVM, the RMSE = 116.89 Wh/m² and r = 93.78%. The results achieved reveal that the proposed hybridization scheme provides a more accurate performance compared to cuckoo search-LS-SVM and the stand-alone LS-SVM. [ABSTRACT FROM AUTHOR]

Accurate and effective forecasting of short-term global solar radiation is critical for the development of photovoltaic systems, particularly for integration into existing grid systems. However, its non-stationary characteristics caused by climatic factors make its estimation extremely challenging. In this regard, a newly designed learning technique for multi-hour global solar radiation forecasting is proposed based on a time-varying filter-empirical mode decomposition (TVF-EMD), feature selection, and extreme learning machine (ELM) as an essence regression. The proposed hybridization strategy consists of three main steps for understanding the fundamental behavioral aspects of hourly global solar radiation data. The first phase employs the TVF-EMD algorithm to deal with the variability of global solar radiation data by separating it into a series of more stable and constant subseries. Then, the feature selection step is employed to evaluate and identify distinctive features set from the whole decomposed subseries by means of the RReliefF algorithm. The selected feature sets are used to train and optimize our forecasting extreme learning machine model, then the tuned ELM model is used to assess the forecasting accuracy. The proposed TVF-EMD-RF-ELM model is evaluated and validated in different regions in Algeria with various climate conditions. The forecasting findings of the TVF-EMD algorithm demonstrate high accuracy compared to the recent version of empirical mode decomposition CEEMDAN. Overall forecasting periods, the TVF-EMD-RF-ELM model produces an error less than 8.3% in terms of normalized root mean square error nRMSE in all studied regions. • A detailed review of different decomposition techniques for global solar radiation forecasting. • An improved artificial intelligence model for multi-hour solar radiation forecasting has been developed. • A regional examination is established, with three sites. • The proposed hybrid model is validated against several models. • The suggested model has been validated for its ability to forecast hourly global solar radiation. [ABSTRACT FROM AUTHOR]

In this paper, a new combined model coupling the linear autoregressive moving average (ARMA) model and the nonlinear artificial neural network (ANN) model has been proposed in order to estimate the daily global solar radiation. The main feature of this approach lies in the fact that has the strength to capture the advantages containing in both models. The combined method have been developed and tested using global solar radiation data recorded during two years (2012–2013) for two different climate sites in Algeria. The obtained results showed an improvement of the combined model over ARMA and ANN models in term of mean absolute error (MPE) of about 18.1% and 2.7%, for the first site, of about 27.26% and 1.39% for the second site. Moreover, compared to the ARMA and ANN models, a decrease in the RMSE values of about 17.1% and 3.59% compared to the ARMA and ANN models has been observed. [ABSTRACT FROM AUTHOR]

This paper presents an overview of actual solar radiation data measurements in Ghardaïa site (32.360 N, 3.810 W, 450 m above MSL). Global solar radiation and surface temperatures were measured and analyzed for one complete year from 1 January-31December 2005. The data thus recorded are compared with corresponding data of the 22-year average of NASA's surface meteorology and solar energy-model. Hourly, daily and monthly solar radiation was made from five-minute recorded by EKO Pyranometer. The highest measured daily and monthly mean solar radiation was found to be 369 and 326 (W/m2), and the highest five minute averaged solar radiation values up to 1268 (W/m2) were observed in the summer season from May to September, and the yearly average daily energy input was 21.83 (MJ/m2/day). Besides the global solar radiation, the daily and monthly average temperature variations are discussed. The collected data indicate that Ghardaïa has a strong potential for solar energy applications. [ABSTRACT FROM AUTHOR]

Precise solar radiation forecasting can provide great benefits and solutions for smart grid distribution and electricity management. However, its non-stationary behavior and randomness render its estimation very difficult. In this respect, a new hybrid learning approach is proposed for multi-hour global solar radiation forecasting, relying on Convolutional Neural Network (CNN), Nonparametric Gaussian Process Regression (GPR), Least Support Vector Machine (LS-SVM), and Extreme Learning Machine (ELM) as essence predictors. Then compressive sensing technique is applied to perform a hybridization scheme of the model's output. Hourly global solar radiation data from two sites in Algeria with different climate conditions are used to evaluate the full potential of the integrated model, with stationarity checks with an advanced clear sky model (MecClear model). Different comparative simulations show the superiority of the proposed pipeline in forecasting hourly global solar radiation data for multi-hour ahead compared to the stand-alone model. Experimental results show that the proposed hybridization methodology can effectively improve the prediction accuracy and outperforms benchmarking models during all the forecasting horizons. [ABSTRACT FROM AUTHOR]

Solar radiation components assessment is a highly required parameter for solar energy applications. Due to the non-stationary behavior of solar radiation parameters and variety of atmosphere conditions, stand-alone forecasting models are insufficient for providing accurate estimation in some cases. In this respect, different hybrid models have been proposed in recent years to overcome the limitations of single models and boost the forecasting precision. In this paper, acomprehensive literature review of the recent trends in hybrid model techniques for solar radiation components assessment is presented. The main objective behind this study is to present a comparative study between different hybrid models, explore their application, and identify promising and potential models for solar radiation application assessment. The performance ranking of each hybrid model is complicated due the diversity of the data length and scale, forecasting horizon, performance metrics, time step and climate condition. Overall, the presented study provides preliminary guidelines for a complete view of the hybrid models and tools that can be used in order to improve solar radiation assessment. • In-depth review of different hybrid methods for solar radiation forecasting is presented. • Forecasting horizon for each category is discussed. • Worldwide regions and data are investigated. [ABSTRACT FROM AUTHOR]

Abstract: In this study, 11 empirical models are developed correlating the monthly mean daily global solar radiation on a horizontal surface with monthly mean sunshine records and air temperature data for six Algerian cities (Algiers, Oran, Batna, Ghardaia, Bechar, and Tamanrasset). In order to indicate their performance, seven statistical parameters were introduced; coefficient of determination (R 2), mean percent error (MPE), mean absolute percent error (MAPE), mean bias error (MBE), mean absolute bias error (MABE), and root mean square error (RMSE). The results obtained in this study confirm the previous studies, which have indicated that the sunshine based models are generally more accurate than air temperature based models. According to the results, the best performances are obtained by the cubic and the quadratic regression models for the six Algerian stations. Moreover, these two regression models can be used for the proposed generalized models for predicting the monthly mean global solar radiation in other Algerian locations in the absence of the measured solar radiation data. [Copyright &y& Elsevier]