Wind Power Plant Prediction by Using Neural Networks
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.
- Authors:
- Publication Date:
- Research Org.:
- National Renewable Energy Lab. (NREL), Golden, CO (United States)
- Sponsoring Org.:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE), Wind and Water Technologies Office (EE-4W)
- OSTI Identifier:
- 1050093
- Report Number(s):
- NREL/CP-5500-55871
Journal ID: ISSN 2329-3721; TRN: US201218%%467
- DOE Contract Number:
- AC36-08GO28308
- Resource Type:
- Conference
- Journal Name:
- IEEE Energy Conversion Congress and Exposition (ECCE)
- Additional Journal Information:
- Journal Volume: 2012; Conference: IEEE Energy Conversion Conference and Exposition, Raleigh, NC (United States), 15-20 Sep 2012; Journal ID: ISSN 2329-3721
- Publisher:
- IEEE
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 17 WIND ENERGY; ACCURACY; ENERGY CONVERSION; FORECASTING; NEURAL NETWORKS; TRAINING; VELOCITY; WIND POWER; WIND POWER PLANTS; wind power plants; wind power prediction; probabilistic neural network; complex-valued recurrent neural network
Citation Formats
Liu, Ziqiao, Gao, Wenzhong, Wan, Yih-Huei, and Muljadi, Eduard. Wind Power Plant Prediction by Using Neural Networks. United States: N. p., 2012.
Web. doi:10.1109/ECCE.2012.6342351.
Liu, Ziqiao, Gao, Wenzhong, Wan, Yih-Huei, & Muljadi, Eduard. Wind Power Plant Prediction by Using Neural Networks. United States. https://doi.org/10.1109/ECCE.2012.6342351
Liu, Ziqiao, Gao, Wenzhong, Wan, Yih-Huei, and Muljadi, Eduard. 2012.
"Wind Power Plant Prediction by Using Neural Networks". United States. https://doi.org/10.1109/ECCE.2012.6342351. https://www.osti.gov/servlets/purl/1050093.
@article{osti_1050093,
title = {Wind Power Plant Prediction by Using Neural Networks},
author = {Liu, Ziqiao and Gao, Wenzhong and Wan, Yih-Huei and Muljadi, Eduard},
abstractNote = {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.},
doi = {10.1109/ECCE.2012.6342351},
url = {https://www.osti.gov/biblio/1050093},
journal = {IEEE Energy Conversion Congress and Exposition (ECCE)},
issn = {2329-3721},
number = ,
volume = 2012,
place = {United States},
year = {Wed Aug 01 00:00:00 EDT 2012},
month = {Wed Aug 01 00:00:00 EDT 2012}
}
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