Abstract
This paper introduces a method of short-term wind power prediction for a wind power plant by training neural networks based on historical data of wind speed and wind direction. The model proposed is shown to achieve a high accuracy with respect to the measured data.
| Original language | American English |
|---|---|
| Number of pages | 9 |
| State | Published - 2012 |
| Event | IEEE Energy Conversion Conference and Exposition - Raleigh, North Carolina Duration: 15 Sep 2012 → 20 Sep 2012 |
Conference
| Conference | IEEE Energy Conversion Conference and Exposition |
|---|---|
| City | Raleigh, North Carolina |
| Period | 15/09/12 → 20/09/12 |
NLR Publication Number
- NREL/CP-5500-55871
Keywords
- complex-valued recurrent neural network
- National Renewable Energy Laboratory (NREL)
- NREL
- probabilistic neural network
- wind power plants
- wind power prediction