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• Fractional calculus is a fast growing field in mathematics, science and engineering. • This is a bibliographic analysis of 351 articles that use Atangana-Baleanu operators • Articles grow by 80.25% yearly from all countries, especially developing countries. • This shows the influence of this rapidly growing field of applied mathematics. Fractional calculus is a branch of mathematical analysis that studies the several different possibilities of defining real number powers or complex number powers of the differentiation and integration operators. Recently, Atangana and Baleanu proposed operators based on generalized Mittag-Leffler functions to solve fractional integrals and derivatives. These contributions have set off an explosion of new research in fractional calculus, and this paper present a comprehensive bibliometric analysis of the peer-reviewed papers inspired by Atangana and Baleanu. In total, 351 papers in the Scopus database have used Atangana-Baleanu operators and this number is growing at 80.25% each year since 2016. These papers were written by 343 authors, predominantly from Mexico, Saudi Arabia and India. Although these data show that Atangana-Baleanu operators support global and fast growing scientific research, the field is dominated statistically by a few productive individuals. I present citation network of the most prolific authors and show collaboration and citation networks. Finally, I present a thematic analysis of the papers applying Atanagana-Baleanu operators and show how research forms clusters based on: analytical solutions, numerical simulations or applications in science and engineering. [ABSTRACT FROM AUTHOR]

Solar energy is considered as one of the main sources for renewable energy in the near future. However, solar energy and other renewable energy sources have a drawback related to the difficulty in predicting their availability in the near future. This problem affects optimal exploitation of solar energy, especially in connection with other resources. Therefore, reliable solar energy prediction models are essential to solar energy management and economics. This paper presents work aimed at designing reliable models to predict the global horizontal irradiance (GHI) for the next day in 8 stations in Saudi Arabia. The designed models are based on computational intelligence methods of automated-design fuzzy logic systems. The fuzzy logic systems are designed and optimized with two models using fuzzy c-means clustering (FCM) and simulated annealing (SA) algorithms. The first model uses FCM based on the subtractive clustering algorithm to automatically design the predictor fuzzy rules from data. The second model is using FCM followed by simulated annealing algorithm to enhance the prediction accuracy of the fuzzy logic system. The objective of the predictor is to accurately predict next-day global horizontal irradiance (GHI) using previous-day meteorological and solar radiation observations. The proposed models use observations of 10 variables of measured meteorological and solar radiation data to build the model. The experimentation and results of the prediction are detailed where the root mean square error of the prediction was approximately 88% for the second model tuned by simulated annealing compared to 79.75% accuracy using the first model. This results demonstrate a good modeling accuracy of the second model despite that the training and testing of the proposed models were carried out using spatially and temporally independent data. [ABSTRACT FROM AUTHOR]

This paper introduces an optimal sizing algorithm for a hybrid renewable energy system using smart grid load management application based on the available generation. This algorithm aims to maximize the system energy production and meet the load demand with minimum cost and highest reliability. This system is formed by photovoltaic array, wind turbines, storage batteries, and diesel generator as a backup source of energy. Demand profile shaping as one of the smart grid applications is introduced in this paper using load shifting-based load priority. Particle swarm optimization is used in this algorithm to determine the optimum size of the system components. The results obtained from this algorithm are compared with those from the iterative optimization technique to assess the adequacy of the proposed algorithm. The study in this paper is performed in some of the remote areas in Saudi Arabia and can be expanded to any similar regions around the world. Numerous valuable results are extracted from this study that could help researchers and decision makers. [ABSTRACT FROM AUTHOR]