http://www.iaeme.com/IJEET/index.asp 106 editor@iaeme.com International Journal of Electrical Engineering & Technology (IJEET) Volume 11, Issue 2, March - April 2020, pp. 106-114. Article ID: IJEET_11_02_013 Available online at http://www.iaeme.com/IJEET/issues.asp?JType=IJEET&VType=11&IType=2 Journal Impact Factor 2020 : 10.1935 (Calculated by GISI) www.jifactor.com ISSN Print: 0976-6545 and ISSN Online: 0976-6553 © IAEME Publication IMPROVED WIND SPEED PREDICTION USING VARIOUS NEURAL NETWORK MODELS Dr. V Ranganayaki* Dr. N.G.P. Institute Technology, India Dr. S N Deepa Anna University Regional Campus, India C. Maheswari Dr. N.G.P. Institute Technology, India *Corresponding author ABSTRACT This work presents the feed forward neural models – Back Propagation Neural Network (BPN) model and Radial Basis Function (RBF) neural models employed to perform wind speed prediction and also the proposed approach of selecting number of hidden neurons embedded into the BPN and RBF models for performing wind speed prediction. Added to this contribution, it proposes hybrid neuro-fuzzy model for the considered application and as well the predicted wind speed employing this approach is compared with that of the basic neuro-fuzzy model i.e., Adaptive Neuro-Fuzzy Inference Systems (ANFIS). This work contribute to carry out effective wind speed prediction so that it enhances the wind system output for renewable resource power generation and the other side on the development of adaptable and scalable neural network architectures with fixed number of hidden layer neurons in BPN model, RBF model and ANFIS model. Considering all three proposed neural network models, the ANFIS network achieves the minimal mean square error resulting in improved wind speed prediction rate. Keywords- Adaptive Neuro-Fuzzy Inference Systems, Back Propagation Network, Mean Square Error, Radial Basis Function. Cite this Article: Dr. V Ranganayaki, Dr. S N Deepa and C. Maheswari, Improved Wind Speed Prediction Using Various Neural Network Models, International Journal of Electrical Engineering and Technology, 11(2), 2020, pp. 106-114. http://www.iaeme.com/IJEET/issues.asp?JType=IJEET&VType=11&IType=2