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Forecasting aggregated wind power production of multiple wind farms using hybrid wavelet‐PSO‐NNs
Author(s) -
Mandal Paras,
Zareipour Hamidreza,
Rosehart William D.
Publication year - 2014
Publication title -
international journal of energy research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.808
H-Index - 95
eISSN - 1099-114X
pISSN - 0363-907X
DOI - 10.1002/er.3171
Subject(s) - particle swarm optimization , intermittency , wind power , wind power forecasting , wind speed , artificial neural network , benchmark (surveying) , hybrid power , computer science , power (physics) , electric power system , meteorology , engineering , algorithm , artificial intelligence , geography , physics , electrical engineering , quantum mechanics , geodesy , turbulence
SUMMARY This paper describes the problem of short‐term wind power production forecasting based on meteorological information. Aggregated wind power forecasts are produced for multiple wind farms using a hybrid intelligent algorithm that uses a data filtering technique based on wavelet transform (WT) and a soft computing model (SCM) based on neural network (NN), which is optimized by using particle swarm optimization (PSO) algorithm. To demonstrate the effectiveness of the proposed hybrid intelligent WT + NNPSO model, which takes into account the interactions of wind power, wind speed, wind direction, and temperature in the forecast process, the real data of wind farms located in the southern Alberta, Canada, are used to train and test the proposed model. The test results produced by the proposed hybrid WT + NNPSO model are compared with other SCMs as well as the benchmark persistence method. Simulation results demonstrate that the proposed technique is capable of performing effectively with the variability and intermittency of wind power generation series in order to produce accurate wind power forecasts. Copyright © 2014 John Wiley & Sons, Ltd.