Abstract
Short-term wind farm power forecasting is studied by exploiting the spatio-temporal correlation between individual turbine's power output. A multivariate time series model for wind farm power generation is developed by using vector autoregression (VAR). In order to avoid the possible over-fitting issues caused by a large number of autoregressive coefficients and the impact on the forecasting performance of VAR models, a sparsified autoregressive coefficient matrix is constructed by utilizing the information on wind direction, wind speed and wind farm's layout. Then, the VAR model parameters are obtained through maximum likelihood estimation of real-time measurement data, by taking into account the sparse structure of the autoregressive coefficient matrix. The proposed approach is compared with univariate autoregressive models through numerical experiments, resulting in significant improvement, which is attributed to the turbine-level correlation captured by the developed VAR model.
| Original language | English (US) |
|---|---|
| Title of host publication | IEEE Power and Energy Society General Meeting |
| Publisher | IEEE Computer Society |
| Volume | 2015-September |
| ISBN (Print) | 9781467380409 |
| DOIs | |
| State | Published - Sep 30 2015 |
| Event | IEEE Power and Energy Society General Meeting, PESGM 2015 - Denver, United States Duration: Jul 26 2015 → Jul 30 2015 |
Other
| Other | IEEE Power and Energy Society General Meeting, PESGM 2015 |
|---|---|
| Country | United States |
| City | Denver |
| Period | 7/26/15 → 7/30/15 |
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Keywords
- Multivariate time series analysis
- short-term wind power forecasting
- vector autoregression
- wind farm
ASJC Scopus subject areas
- Energy Engineering and Power Technology
- Nuclear Energy and Engineering
- Renewable Energy, Sustainability and the Environment
- Electrical and Electronic Engineering
Cite this
A sparsified vector autoregressive model for short-term wind farm power forecasting. / He, Miao; Vittal, Vijay; Zhang, Junshan.
IEEE Power and Energy Society General Meeting. Vol. 2015-September IEEE Computer Society, 2015. 7285972.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A sparsified vector autoregressive model for short-term wind farm power forecasting
AU - He, Miao
AU - Vittal, Vijay
AU - Zhang, Junshan
PY - 2015/9/30
Y1 - 2015/9/30
N2 - Short-term wind farm power forecasting is studied by exploiting the spatio-temporal correlation between individual turbine's power output. A multivariate time series model for wind farm power generation is developed by using vector autoregression (VAR). In order to avoid the possible over-fitting issues caused by a large number of autoregressive coefficients and the impact on the forecasting performance of VAR models, a sparsified autoregressive coefficient matrix is constructed by utilizing the information on wind direction, wind speed and wind farm's layout. Then, the VAR model parameters are obtained through maximum likelihood estimation of real-time measurement data, by taking into account the sparse structure of the autoregressive coefficient matrix. The proposed approach is compared with univariate autoregressive models through numerical experiments, resulting in significant improvement, which is attributed to the turbine-level correlation captured by the developed VAR model.
AB - Short-term wind farm power forecasting is studied by exploiting the spatio-temporal correlation between individual turbine's power output. A multivariate time series model for wind farm power generation is developed by using vector autoregression (VAR). In order to avoid the possible over-fitting issues caused by a large number of autoregressive coefficients and the impact on the forecasting performance of VAR models, a sparsified autoregressive coefficient matrix is constructed by utilizing the information on wind direction, wind speed and wind farm's layout. Then, the VAR model parameters are obtained through maximum likelihood estimation of real-time measurement data, by taking into account the sparse structure of the autoregressive coefficient matrix. The proposed approach is compared with univariate autoregressive models through numerical experiments, resulting in significant improvement, which is attributed to the turbine-level correlation captured by the developed VAR model.
KW - Multivariate time series analysis
KW - short-term wind power forecasting
KW - vector autoregression
KW - wind farm
UR - http://www.scopus.com/inward/record.url?scp=84956855138&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84956855138&partnerID=8YFLogxK
U2 - 10.1109/PESGM.2015.7285972
DO - 10.1109/PESGM.2015.7285972
M3 - Conference contribution
SN - 9781467380409
VL - 2015-September
BT - IEEE Power and Energy Society General Meeting
PB - IEEE Computer Society
ER -