Wind speed and power forecasting based on spatial correlation models
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.
- Research Organization:
- Aristotle Univ. of Thessaloniki (GR)
- OSTI ID:
- 20001196
- Journal Information:
- IEEE Transactions on Energy Conversion (Institute of Electrical and Electronics Engineers), Journal Name: IEEE Transactions on Energy Conversion (Institute of Electrical and Electronics Engineers) Journal Issue: 3 Vol. 14; ISSN 0018-9383; ISSN ITCNE4
- Country of Publication:
- United States
- Language:
- English
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