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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]

Abstract Accurate estimation of solar radiation components of a specific location has been one of the most important issues of solar energy applications. In this paper, a new approach, named Weighted Gaussian Process Regression (WGPR), is developed for multi-step ahead forecasting of daily global and direct horizontal solar radiation components in Saharan climate. The WGPR is tested using global and direct solar radiation data recorded over three years (2013–2015) in a semi-arid region in Algeria. It consists of forecasting 10-steps ahead for both components with automatic selection of relevant climatic data. In this respect two different architectures of WGPR are proposed, WGPR Parallel Forecasting Architecture (WGPR-PFA) and WGPR Cascade Forecasting Architecture (WGPR-CFA). The proposed approach proved to be effective with respect to the basic GPR in terms of accuracy and processing time for daily global and direct solar radiation forecasting. Forecasting with WGPR-CFA led to error RMSE = 3.18 (MJ/m2) and correlation coefficient r2 = 85.85 (%) for the 10th daily global horizontal radiation, and RMSE = 5.23 (MJ/m2) and correlation coefficient r2 = 56.21(%) for 10th daily direct horizontal radiation. The achieved results specify that the developed WGPR approach can be adjudged as an efficient machine learning model for accurate forecasting of solar radiation components. Graphical abstract Image Highlights • A new GPR model is developed for multi-step ahead forecasting. • Two different architectures of WGPR are proposed. • A case study of Ghardaïa regionhas been considered in this work. [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]

[Display omitted] • A new hybrid model IF-ELM is developed for multi-step ahead forecasting of PV power. • Two different architectures of IF-ELM are proposed. • Three PV plants database are analyzed. • Proposed IF-ELM boost the forecasting performance of PV power output compared to existing approaches. Photovoltaic (PV) solar power, which is considered as the most competitive clean energy source, contributes to a significant percentage of electricity production in many developed countries. However, accurate PV power forecasting is necessary due to its high variation that can be caused by several factors. Hence, the intermittent nature of PV production represents a major challenge to integrate PV systems into the electric grid. The scope of this paper deals with this issue through developing a new integrated PV power forecasting model. The proposed model is based on the use of a new decomposition methodology, named Iterative Filtering for decomposing PV power into different intrinsic functions (IMFs), then Extreme Learning Machine (ELM) is used as essence predictor. To this end, the proposed IF-ELM model is evaluated on three solar PV power plants installed at three different sites, with different climatic conditions. Direct and recursive IF-ELM methodologies are examined for multi-step ahead forecasting in a very short time-scale (up to 60 min). Overall, the forecasting results show high precision performance for the studied forecasting horizons- in terms of different statistical metrics compared to stand-alone models. Also, the proposed IF method shows its high performance when compared to the recently developed decomposition method. complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) in improving the forecasting accuracy of a single model. Forecasting results with the IF-ELM model led to an error in nRMSE that is less than 10% and a Correlation Coefficient greater than 98% over all 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]

• Comparison of optimized spiral and staggered layouts for different heliostat shapes. • The GOA algorithm is used for the first time to optimize the heliostat layout. • A circular heliostat shape reduces the land area by 25.9% & 28.4% for both layouts. • A circular heliostat shape improves the efficiency to 71% & 71.1% for both layouts. During the design and characterization of Solar Power Tower (SPT) systems, the selection and optimization of the heliostat field distribution are critical. Additionally, selecting the appropriate heliostat shape is an important factor to consider because of the optical losses linked to the heliostat shape. In the current study, the optimization and comparison between biomimetic spiral and Campo radial-staggered layout are carried out by considering various heliostat shapes. The optimization is carried out for the PS10 plant installed in Spain. The Grasshopper Optimization Algorithm (GOA) approach is used, and the heliostat field efficiency is assessed. The optimized heliostat field showed that the Campo radial-staggered layout provides better efficiency and the lowest land area when compared to the biomimetic spiral layout. In addition, using a circular heliostat shape for the original PS10 field, the land area could be reduced by 25.86% (about 113,538 m2) and 28.39% (about 124,633 m2) for the biomimetic spiral and staggered fields, respectively. On the other hand, for the circular heliostat, the yearly efficiency is improved to ∼ 71 and ∼ 71.1% for biomimetic spiral and staggered fields, respectively, compared to the conventional rectangular heliostat with an efficiency equal to ∼ 69.3%. Indeed, the registered gains in land area and yearly efficiency will reduce the Levelized cost of energy. [ABSTRACT FROM AUTHOR]