Hybrid RNN-LSTM deep learning model applied to a fuzzy based wind turbine data uncertainty quantization method

Abstract

In addition to the plenty of advantages that the penetration of wind turbine (WT) brings to the smart networks, uncertainty problems can be considered as an unavoidable phenomenon that requires to be addressed. The results of high uncertainty are able to lead to the instability of management plans and schedules that is able to lead to the serious issues for operators and users. The following case study tries to propose a novel reinforcement learning based hybrid-based quantification technique to capture the prediction fault into the output power of WT. The offered approach has been applied the hybrid recurrent neural network (RNN) and long-short term memory (LSTM) layout with the aim of learning the utmost efficient Spatio-temporal properties of WT's output power. Because of the wide complication of the information, a novel optimization approach according to the modified sine cos algorithm has been suggested to aid in further steady layout training. The possibility and efficacy of the model are evaluated via the test analysis of two datasets in wind lands of Australian.