Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/3585
Title: Short-term Wind Power Forecasting using Wavelet based Recurrent Wavelet Neural Network for Small-Scale Wind Turbine
Authors: Pradhan, Prangya Parimita
Subudhi, Bidyadhar
Ghosh, Arnab
Keywords: RNN
RWNN
MODWT
Issue Date: Sep-2021
Publisher: IEEE
Citation: 2021 International Symposium of Asian Control Association on Intelligent Robotics and Industrial Automation (IRIA), IIT Goa, India, 20-22 September 2021
Abstract: To estimate wind energy availability for stable economic scheduling in the electricity market, it is necessary to forecast wind power. However, the uncertain nature of wind, makes the estimation difficult. So, to achieve good wind power estimation, a wind speed forecast is necessary. An hourlybased wind speed data is used to predict wind power up to 24 hours ahead for a small wind power plant (> 5kW). The proposed method consists of two steps as wavelet-based recurrent neural network (RWNN) is used for wind speed estimation and the second step uses these estimated speed samples to predict the wind turbine power. The results are compared to those obtained using the traditional RNN technique. The effectiveness of the result is shown by mean absolute error.
Description: Copyright of this paper is with proceedings publisher
URI: http://hdl.handle.net/2080/3585
Appears in Collections:Conference Papers

Files in This Item:
File Description SizeFormat 
2021_IRIA21_PPPrahan_Short-term.pdf2.11 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.