A novel hybrid approach based on relief algorithm and fuzzy reinforcement learning approach for predicting wind speed

Wind speed (WS) prediction has become popular nowadays due to increasing demand for wind power generation and competitive development in energy. Many models are used predict WS which is non-stationary, nonlinear irregular. However, they neglect the effectiveness of feature selection methods prediction, thereby creating very challenging precise safe operation industry. To overpower these challenges further improve accuracy, a model developed based on technique models. Therefore this study proposes an adaptive self-learning predicting using fuzzy reinforcement learning (FRL) that Fuzzy Q Learning (FQL). Proposed FQL predictor can with great accuracy. This first effort at developing forecasting FRL prediction. The presented no prior knowledge system or plant target information. Measured processed through Info Gain attribute evaluator Ranker search method purpose serves as input model. comparison proposed existing machine carried out simulations. performance analysis indicates important tool potential assessment.