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Title: Wind speed and power forecasting based on spatial correlation models

Abstract

Wind Energy Conversion systems (WECS) cannot be dispatched like conventional generators. This can pose problems for systems schedulers and dispatchers, especially if the schedule of wind power availability is not known in advance. However, if the wind speed can be reliably forecasted up to several hours ahead, the generating schedule can efficiently accommodate the wind generation. This paper illustrates a technique for forecasting wind speed and power output up to several hours ahead, based on cross correlation at neighboring sites. The authors develop an Artificial Neural Network (ANN) that significantly improves forecasting accuracy comparing to the persistence forecasting model. The method is tested at different sites over a one-year period.

Authors:
; ;
Publication Date:
Research Org.:
Aristotle Univ. of Thessaloniki (GR)
OSTI Identifier:
20001196
Resource Type:
Journal Article
Journal Name:
IEEE Transactions on Energy Conversion (Institute of Electrical and Electronics Engineers)
Additional Journal Information:
Journal Volume: 14; Journal Issue: 3; Other Information: PBD: Sep 1999; Journal ID: ISSN 0018-9383
Country of Publication:
United States
Language:
English
Subject:
17 WIND ENERGY; WIND POWER PLANTS; OPERATION; NEURAL NETWORKS; ARTIFICIAL INTELLIGENCE; WIND POWER; FORECASTING

Citation Formats

Alexiadis, M.C., Dokopoulos, P.S., and Sahsamanoglou, H.S. Wind speed and power forecasting based on spatial correlation models. United States: N. p., 1999. Web. doi:10.1109/60.790962.
Alexiadis, M.C., Dokopoulos, P.S., & Sahsamanoglou, H.S. Wind speed and power forecasting based on spatial correlation models. United States. doi:10.1109/60.790962.
Alexiadis, M.C., Dokopoulos, P.S., and Sahsamanoglou, H.S. Wed . "Wind speed and power forecasting based on spatial correlation models". United States. doi:10.1109/60.790962.
@article{osti_20001196,
title = {Wind speed and power forecasting based on spatial correlation models},
author = {Alexiadis, M.C. and Dokopoulos, P.S. and Sahsamanoglou, H.S.},
abstractNote = {Wind Energy Conversion systems (WECS) cannot be dispatched like conventional generators. This can pose problems for systems schedulers and dispatchers, especially if the schedule of wind power availability is not known in advance. However, if the wind speed can be reliably forecasted up to several hours ahead, the generating schedule can efficiently accommodate the wind generation. This paper illustrates a technique for forecasting wind speed and power output up to several hours ahead, based on cross correlation at neighboring sites. The authors develop an Artificial Neural Network (ANN) that significantly improves forecasting accuracy comparing to the persistence forecasting model. The method is tested at different sites over a one-year period.},
doi = {10.1109/60.790962},
journal = {IEEE Transactions on Energy Conversion (Institute of Electrical and Electronics Engineers)},
issn = {0018-9383},
number = 3,
volume = 14,
place = {United States},
year = {1999},
month = {9}
}