Advanced neural network and hybrid models for wind power forecasting: a comprehensive global review

Abstract

Neural Network Algorithms (NNAs), modeled after the workings of biological neurons, are increasingly utilized in areas like data mining and robotics to address complex challenges in artificial intelligence (AI). This research will undertake a systematic review based on advanced neural networks and hybrid models for wind power forecasting. Using the Scopus database, a methodical search, acquisition, and filtering procedure was utilized to find pertinent publication documents; VOSviewer software was utilized to analyze trends. The emphasis on improving prediction accuracy and stability in wind power forecasting through the application of cutting-edge machine learning techniques and hybrid models is a prominent feature that unites the literature. Furthermore, attention is being paid to resolving issues pertaining to the production of wind energy, such as wind power fluctuation management, grid integration problems, wind speed prediction, and turbine health monitoring. A rising trend involves multi-dimensional, multi-step forecasting and incorporating factors like weather data and spatial-temporal features to enhance reliability. This paper contributes by exploring the integration of optimization techniques with neural networks, investigating hybrid models to improve wind power predictions, assessing LSTM-based approaches in forecasting, and suggesting directions for future research.