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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) IAES International Journal of Artificial Intelligence (IJ-AI)
M. Nandana Jyothi
Acharya Nagarjuna University
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Very-Short Term Wind Power Forecasting through Wavelet Based ANFIS M. Nandana Jyothi; P.V. Ramana Rao
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 9, No 1: March 2018
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1073.802 KB) | DOI: 10.11591/ijpeds.v9.i1.pp397-405

Abstract

This paper proposes a Wavelet based Adaptive Neuro-Fuzzy Inference System (WANFIS) applied to forecast the wind power and enhance the accuracy of one step ahead with a 10 minutes resolution of real time data collected from a wind farm in North India. The proposed method consists two cases. In the first case all the inputs of wind series and output of wind power decomposition coefficients are carried out to predict the wind power. In the second case all the inputs of wind series decomposition coefficients are carried out to get wind power prediction. The performance of proposed WANFIS is compared to Wavelet Neural Network (WNN) and the results of the proposed model are shown superior to compared methods.
Narx Based Short Term Wind Power Forecasting Model M. NANDANA JYOTHI; V. DINAKAR; N. S S RAVI TEJA
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 4, No 4: December 2015
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (597.742 KB) | DOI: 10.11591/ijai.v4.i4.pp129-138

Abstract

This paper contributes a short-term wind power forecasting through Artificial Neural Network with nonlinear autoregressive exogenous inputs (NARX) model. The meteorological parameters like wind speed, temperature, pressure, and air density are considered as input parameters collected from KL University area and the calculated generated power as output parameters of neural network to predict the wind power generation. Based on hybrid forecasting technique a code is developed in MATLAB at different hidden layers and delay times.
Co-Authors N. S S RAVI TEJA P.V. Ramana Rao V. DINAKAR