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Integrating photovoltaic power into the power system can offer significant economic and environmental benefits. However, the intermittent and random nature of photovoltaic power generation poses a challenge to the current power system's planning and operation. Accurate photovoltaic power generation forecasting is crucial for delivering high-quality electric energy to consumers and increasing system reliability.In this study, a multi-stage approach is proposed. In the first stage, three decomposition techniques (Iterative Filtering decomposition, Variational Mode Decomposition, and Wavelet Packet Decomposition) are employed for time series decomposition. Then, for each Intrinsic Mode Function (IMF) component resulting from the decomposition block, five machine learning and three deep learning algorithms are utilized, serving as local forecasting models. In the final forecast phase, the best forecasting result for each regressor is selected during the reconstruction phase. Two years of photovoltaic power data recorded in three grid-connected photovoltaic systems installed in South Algeria were utilized for training and testing the proposed forecasting models. Upon comprehensive analysis and examination of the outcomes, the proposed method exhibits the lowest normalized Root Mean Square Error values across all forecast horizons and monitoring stations. Particularly, for forecasting steps at time intervals +1, +3, and +5, the proposed method attains an average normalized Root Mean Square Error, showcasing its efficacy: 0.709%, 2.097%, and 3.241% for station 1; 1.147%, 3.546%, and 5.347% for station 2; and 0.922%, 2.158%, and 4.539% for station 3. The experimental results underscore the superiority of our approach over conventional regression algorithms, thus substantiating its prowess in delivering robust and competitive performance outcomes. [ABSTRACT FROM AUTHOR]

• •Development of an enhanced hybrid model for multi-hour ahead DNI forecasting. • •Comprehensive performance analysis using four different DNI databases. • •Implementation of the GA-GOA Algorithm for CSP Optimization. • •Assessment of electricity generation from CSP plants for multi-hour ahead forecasting. This study addresses the critical need for precise Direct Normal Irradiation forecasting in concentrating solar power systems to enhance performance and manage power generation intermittency. We propose a novel hybrid model that combines Variation Mode Decomposition, Swarm Decomposition Algorithm, Random Forest for feature selection, and Deep Convolutional Neural Networks, aiming to improve the forecasting accuracy. This model covers the entire process from Direct Normal Irradiation forecasting to heliostat field optimization and electricity generation. We validated the model across four globally diverse regions, taking into account their distinct climates and meteorological conditions. The results show that our model aligns closely with actual measurements and outperforms existing forecasting methods in terms of precision and stability. The forecasting performance was assessed using normalized Root Mean Square Error, with results ranging from 0.75% to 3.4% across different regions. This demonstrates the model's potential for real-world application in concentrating solar power systems, optimizing heliostat field effectiveness, and reliably forecasting electricity production for grid management. [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]

A comprehensive knowledge of available solar energy amount in a specific location is crucial for the successful development for solar energy policies and investment. Indeed, PV or CSP systems require reliable information on solar radiation measurements. This could be obtained from solar radiation data measured by meteostations installed in different places of the site under consideration. In this paper, an overview of solar radiation measurements assessment in Ghardaïa region has been analysed. The choice of this site is based on its membership to the enerMENA meteorological network. The investigation of the data has exposed a huge solar potential where the average annual global solar radiation received on horizontal surface is about 2149 kWh/m2 and the DNI about of 2305 kWh/m2. The comparison with satellite derived data has provided and error of about 4.67%. [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]

A comparative analysis has been carried out between eight metaheuristic algorithms, namely; genetic algorithm (GA), differential evolution (DE), particle swarm optimization (PSO), grey wolf optimization (GWO), improved grey wolf optimization (IGWO), artificial bee colony (ABC), grasshopper optimization algorithm (GOA), and a proposed hybrid genetic-grasshopper (GA-GOA) to optimize the staggered heliostat field of the PS10 plant. The annual weighted efficiency is taken as the objective function for field layout optimization. In addition, the investigated algorithms have been assessed in terms of best energy yield, levelized cost of energy (LCOE), land use factor (LUF), and computational cost. It has been found that evolutionary algorithms outperform swarm intelligence algorithms in terms of efficiency, whereas GOA and GWO converge faster. To get high efficiency with low computational cost, a hybrid GA-GOA algorithm has been proposed. This study found that the hybrid GA-GOA algorithm does indeed deliver improved performance, with an optimum weighted efficiency boosted by 1.45% at a computation cost of ∼63.7 h. In addition, it provides the best optimum LCOE of 26.22 c€/kWh and successfully enhances LUF by 11.2% compared to the PS10 reference plant. Based on these results, the authors can conclude that the proposed hybrid GA-GOA algorithm represents a suitable tool to cost-effectively optimize the design of heliostat field layouts and reduce their land footprint. [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]

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]