NDLI: Missing wind data forecasting with adaptive neuro-fuzzy inference system

Content Provider	Springer Nature Link
Author	Hocaoglu, Fatih O. Oysal, Yusuf Kurban, Mehmet
Copyright Year	2008
Abstract	In any region, to begin generating electricity from wind energy, it is necessary to determine the 1-year distribution characteristics of wind speed. For this aim, a wind observation station must be constructed and 1-year wind speed and direction data must be collected. For determining the distribution characteristics, the collected data must be statistically analyzed. The continuity and reliability of the data are quite important for such studies on the days when possible faults can occur in any part of the observation unit or on days when, the system is on maintenance, it is not possible to record any data. In this study, it is assumed that the station had not worked at some randomly chosen days and that for these days no data could be recorded. The missing data are predicted using the data that were recorded before and after fault or maintenance by an adaptive neuro-fuzzy inference system (ANFIS). It is seen that ANFIS is successful for such a study.
Starting Page	207
Ending Page	212
Page Count	6
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	18
Issue Number	3
e-ISSN	14333058
Language	English
Publisher	Springer-Verlag
Publisher Date	2008-01-31
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Wind energy Wind speed Forecasting Missing data ANFIS Back-propagation Simulation and Modeling
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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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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