Advanced SearchSearch Tips
Short-term Wind Farm Power Forecasting Using Multivariate Analysis to Improve Wind Power Efficiency
facebook(new window)  Pirnt(new window) E-mail(new window) Excel Download
 Title & Authors
Short-term Wind Farm Power Forecasting Using Multivariate Analysis to Improve Wind Power Efficiency
Wi, Young-Min;
  PDF(new window)
This paper presents short-term wind farm power forecasting method using multivariate analysis and time series. Based on factor analysis, the proposed method makes new independent variables which newly composed by raw independent variables such as wind speed, ramp rate, wind power. Newly created variables are used in the time series model for forecasting wind farm power. To demonstrate the improved accuracy, the proposed method is compared with persistence model commonly used as reference in wind power forecasting using data from Jeju Island. The results of case studies are presented to show the effectiveness of the proposed forecasting method.
Wind Power Forecasting;Multivariate Analysis;Time Series Model;
 Cited by
시계열 모형을 이용한 단기 풍력 단지 출력 지역 통합 예측에 관한 연구,위영민;이재희;

전기학회논문지, 2016. vol.65. 6, pp.918-922 crossref(new window)
곡선회귀분석을 이용한 풍력발전 출력 예측에 관한 연구,최영도;정솔영;박범준;허진;박상호;윤기갑;

KEPCO Journal on Electric Power and Energy, 2016. vol.2. 4, pp.627-630 crossref(new window)
EWEA, Wind energy scenarios for 2020, Jul. 2014.

Y Kang, The importance of national energy security and wind energy, KONEPA, 2014.

G Giebel, L Lnadberg, G Kariniotakis, and R Brownsword, State-of-the-Art on Methods and Software Tools for Short-Term Prediction wind energy production, Proc. of the 2003 European Wind Energy Association Conference EWEC'03, Jul. 2003.

K Kim, Y Park, J Park, K Ko, and J Huh, SFeasibility Study on Wind Power Forecasting Using MOS Forecasting Result of KMA, JKSES, vol. 30, no. 2, 2010.

G Sideratos and N Hatziargyriou, An Advanced Statistical Method for Wind Power Forecasting, IEEE Transaction on Power Systems, Vol. 22, No. 1, pp. 258-265. 2007. crossref(new window)

M Negnevitsky and C Potter, Innovative Short-term Wind Generation Prediction Techniques, IEEE Power System Conference and Exposition, pp 60-65, 2006.

T El-Fouly, E El-Saadany, and M Salama, Grey Predictor for Wind Energy Conversion Systems Output Power Prediction" IEEE Transaction on Power Systems, Vol. 21, No. 3, pp. 1450-1452. 2006. crossref(new window)

J Palomares-Salas, J Rosa, J Ramiro, J Melgar, A Aguera, and A Moreno, ARIMA vs. Neural Networks for Wind Speed Forecasting, CIMSA 2009-International Conference on Computational Intelligence for Measurement Systems and Applications, 2009.

I Damousis, M Alexiadis, J Theocharis, and P Dokopoulos, A Fuzzy Model for Wind Speed Prediction and Power Generation in Wind Parks using Spatial Correlation, IEEE Transaction on Energy Conversion, Vol. 19, No. 2, pp. 352-361. 2008.

H Lee, J Cho, W Park, and J Kim, G. Song, Short-term Reactive Power Load Forecasting Using Multiple Time-Series Model, Journal of KIIEE, vol. 18, no. 5, pp. 105-111, Sep. 2004.

S Choi and H Kim, Short-term Demand Forecasting Using Data Mining Method, Journal of KIIEE, vol. 21, no. 10, pp. 126-133, Dec. 2007.

T Han and E Nahm, The Development of Model for the Prediction of Water Demand using Kalman Filter Adaptation Model in Large Distribution System, Journal of KIIEE, vol. 15, no. 2, pp. 38-48, Mar. 2001.

Haiyang Zheng and Andrew Kusiak, Prediction of Wind Farm Power Ramp Rates: A Data-Mining Approach, Journal of Solar Energy Engineering, vol. 131, Aug. 2009.

C Lee, C Park, J Kim, and J Back, A Study on Improving Classification Performance for Manufacturing Process Data with Multicollinearity and Imbalanced Distribution, Journal of the Korean Institute of Industrial Engineers, vol. 41, no. 1, pp. 25-33, Feb. 2015. crossref(new window)

Kanna B. and S.N. Singh, "AWNN-Assisted Wind Power Forecasting Using Feed-Forward Neural Network", IEEE Trans. Sustain. Energy, vol. 3, no 2., pp. 306-315, Apr. 2012. crossref(new window)