Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges

Abstract The integration of large-scale wind power introduces issues in modern power systems operations due to its strong randomness and volatility. These issues can be resolved via wind power forecasting that can provide comprehensive future information about wind power uncertainties. This paper presents a timely and comprehensive review of meta-heuristic algorithms in the framework of wind power forecasting. The framework is based on the auxiliary layer, forecasting base layer, and core layer. The auxiliary layer, such as the data-decomposition layer, decomposes the wind power time series into many relatively stationary subseries, and uses prediction models, including artificial neural networks (ANNs) and machine learning (ML). The core layer is based on meta-heuristic algorithms, which include evolutionary-based algorithms, physics-based algorithms, human-based algorithms, swarm-based algorithms, hybrid algorithms, and multi-objective optimization algorithms. These algorithms aim to search for the optimal solutions under constraints, which is highly significant for optimizing the key parameters of the prediction models. Besides, multiple error evaluation metrics, e.g., deterministic, uncertainty, and testing methods used in the field of wind power prediction are described. A quantitative analysis focusing on their advantages, disadvantages, forecasting accuracy, and computational costs are also provided. Finally, a few open research issues and trends related to the topic are discussed, which can contribute to improving the understanding of each wind power forecasting method. In general, this review paper provides valuable insights to wind power engineers.