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Wind speed forecasting based on wavelet transformation and recurrent neural network
Author(s) -
Pradhan Prangya Parimita,
Subudhi Bidyadhar
Publication year - 2019
Publication title -
international journal of numerical modelling: electronic networks, devices and fields
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.249
H-Index - 30
eISSN - 1099-1204
pISSN - 0894-3370
DOI - 10.1002/jnm.2670
Subject(s) - wind speed , wind power , computer science , renewable energy , artificial neural network , wind power forecasting , wavelet , convergence (economics) , transformation (genetics) , recurrent neural network , energy (signal processing) , wavelet transform , power (physics) , electric power system , control theory (sociology) , meteorology , artificial intelligence , engineering , mathematics , statistics , economic growth , chemistry , biochemistry , control (management) , quantum mechanics , physics , electrical engineering , economics , gene
With the increase in power demand, renewable energy resources such as wind energy have been developed as one of the fastest growing energy sources. However, the wind power generation system depends on the availability of wind flow. The intermittence nature of wind speed is the most serious concern in wind energy application into the existing power system. Hence, wind speed forecasting approaches are proposed in order to deal with these problems. In this paper, a hybrid model of wind speed forecasting is developed. The proposed hybrid model consists of two steps: the first one is decomposition of wind speed sample data by wavelet technique, and the second step uses these decomposed data to estimate wind speed through recurrent wavelet neural network (RWNN). To validate the proposed model, it is compared with the conventional recurrent neural network (RNN) prediction structure. The obtained results based on real data provide the effectiveness of the proposed model in terms of mean absolute error and the rate of convergence parameter.