NDLI: A new strategy for wind speed forecasting using hybrid intelligent models

Content Provider	IEEE Xplore Digital Library
Author	Haque, A.U. Mandal, P. Meng, J. Kaye, M.E. Liuchen Chang
Copyright Year	2012
Description	Author affiliation: Dept. of Ind., Univ. of Texas at El Paso, El Paso, TX, USA (Mandal, P.) \|\| Dept. of Electr. & Comput. Eng., Univ. of New Brunswick, Fredericton, NB, Canada (Haque, A.U.; Meng, J.; Kaye, M.E.; Liuchen Chang)
Abstract	Predicting wind power is considered as one of the most important tasks for the large-scale integration of intermittent wind-powered generators into power systems. Given the cubic relationship between wind speed and wind power, accurate forecasting of wind speed is important for the estimation of future wind power generation output. This paper presents a short-term wind speed forecasting technique using a hybrid intelligent algorithm that utilizes a data filtering technique based on wavelet transform (WT) and a soft computing model (SCM) based on fuzzy ARTMAP (FA) network. The effectiveness of the proposed hybrid WT+FA model is evaluated by comparing it with various other SCMs as well as hybrid models. The test results show that a significant reduction in forecast error of an individual FA network by more than 40% through the application of a combined FA and WT. The forecasting performance of the proposed WT+FA is not only robust and more effective than that of individual FA network but also it shows superiority over other considered SCMs. The forecasting techniques were tested using the real data from the North Cape wind farm located in PEI, Canada.
Sponsorship	IEEE Montreal Sect.
Starting Page	1
Ending Page	4
File Size	604689
Page Count	4
File Format	PDF
ISBN	9781467314312
ISSN	08407789
e-ISBN	9781467314336
e-ISBN	9781467314329
DOI	10.1109/CCECE.2012.6334847
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-04-29
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Wind speed Forecasting Predictive models Wind forecasting Artificial neural networks Data models Autoregressive processes wavelet transform Short-term wind speed forecasting soft computing models fuzzy ARTMAP network
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Hardware and Architecture

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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