Review of current artificial intelligence methods and metaheuristic algorithms for wind power prediction

Doha Bouabdallaoui, Touria Haidi, Mariam El Jaadi


Due to the insufficient fossil resources and the increasing environmental challenges, the world is heading for a more use-oriented to renewable energy sources, specifically to wind energy. A number of predictive techniques are available for the efficient use of wind energy. This article, which is a review of methods of artificial intelligence (AI) and meta-heuristic algorithms for wind energy prediction, fits into this context. There are two distinct categories: the first consists of traditional methods that are commonly used in this context, like different types of artificial neural networks (ANN), support vector machines (SVM) and fuzzy logic; the second is a combined approach which mixes the classic artificial intelligence methods and the meta-heuristic algorithms for the optimization of the forecast output. Then, a summary and comparison between the methodologies are established, and the advantages and limits of each technique are defined. The combination of the classic artificial intelligence and metaheuristic algorithms has a greater performance than the utilization of classic methods only. Nevertheless, using hybrid metaheuristic algorithms with classic artificial intelligence prediction methods can provide a higher precision.


Artificial intelligence; Meta-heuristic algorithms; Prediction; Renewable energy; Wind power

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