The production of energy from renewable wind sources is significantly affected by dynamic changes in wind speed and force, environmental parameters, and turbine operating conditions. These factors play a key role in reliability studies and wind energy production forecasting. In this context, the integration of remote sensing (such as high-frequency radars and satellite data) with artificial neural networks (ANN) provides an effective tool for accurate wind force prediction, and numerous studies with various but related objectives have been conducted to estimate real-time reliability and energy production. This article offers a comprehensive review of the literature on the application of remote sensing and ANN in predicting wind behavior for renewable energy production. Special focus is placed on describing the scope of case studies (such as wind forecasting), ranging from simple ANN models to hybrid deep learning approaches, and key variables like wind speed, direction, and climatic data. This study highlights research that utilizes these technologies to predict reliability issues and develop preventive maintenance policies.
