초록

The importance of wind energy has been increasing. Most of countries have studied for generating effective forecasting models about wind power prediction. This study follows up research about statistical wind forecasting models from 2000 to 2016 and compares each model in several aspects. Variety of estimators measure such as mean squared error and correlation coefficient that used to compare different models are defined. Most of methods use wind speed for dependent and independent variable. The extended application of artificial neural network and ARIMA model have mainly used to predict wind power in the past. The state-of-the-art of focuses on nonlinear regression, feature selection and Ensemble methods which are different from classical ANN and ARIMA.

키워드

풍력 예측 모형, 예측 평가 척도, 인공 신경망 모형, 회귀 모형, 시게열 모형

참고문헌(18)open

  1. [인터넷자료] 송명규 / 국내 풍력, ‘제주·강원’ 가장 많아

  2. [학술지] Bechrakis, D. A. / 2000 / Simulation of the Wind Speed at Different Heights Using Artificial Neural Networks / Wind Engineering 24 (2) : 127 ~ 136

  3. [학술지] Sfetsos, A. / 2002 / A Novel Approach for the Forecasting of Mean Hourly Wind Speed Time Series / Renewable Energy 27 (2) : 163 ~ 174

  4. [학술지] More, A. / 2003 / Forecasting Wind with Neural Networks / Marine structures 16 (1) : 35 ~ 49

  5. [학술지] Campbell, P. R. J. / 2005 / A Novel Approach to Wind Forecasting in the United Kingdom and Ireland / International Journal Of Simulation Systems 6 (12-13) : 1 ~ 10

  6. [학술지] Potter, C. W. / 2006 / Very Short-term Wind Forecasting for Tasmanian Power Generation / IEEE Transactions on Power Systems 21 (2) : 965

  7. [학술지] Barbounis, T. G. / 2006 / Long-Term Wind Speed and Power Forecasting Using Local Recurrent Neural Network Models / IEEE Transactions on Energy Conversion 21 (1) : 273 ~ 284

  8. [학술지] Barbounis, T. G. / 2007 / A Locally Recurrent Fuzzy Neural Network with Application to the Wind Speed Prediction Using Spatial Correlation / Neurocomputing 70 (7) : 1525 ~ 1542

  9. [학술지] Kavasseri, R. G. / 2009 / Day-ahead Wind Speed Forecasting Using f-ARIMA Models / Renewable Energy 34 (5) : 1388 ~ 1393

  10. [학술지] Baile, R. / 2011 / Short‐Term Forecasting of Surface Layer Wind Speed Using a Continuous Random Cascade Model / Wind Energy 14 (6) : 719 ~ 734

  11. [학술지] Poitras, G. / 2011 / Wind Speed Prediction for a Target Station Using Neural Networks and Particle Swarm Optimization / Wind Engineering 35 (3) : 369 ~ 380

  12. [학술지] Cao, Q. / 2012 / Forecasting Wind Speed with Recurrent Neural Networks / European Journal of Operational Research 221 (1) : 148 ~ 154

  13. [학술대회] Liu, Z. / 2012 / Wind Power Plant Prediction by Using Neural Networks / 2012 IEEE Energy Conversion Congress and Exposition : 3154 ~ 3160

  14. [학술지] Vladislavleva, E. / 2013 / Predicting the Energy Output of Wind Farms Based on Weather Data: Important Variables and Their Correlation / Renewable energy 50 : 236 ~ 243

  15. [학술지] Messner, J. W. / 2014 / Probabilistic Wind Power Forecasts with an Inverse Power Curve Transformation and Censored Regression / Wind Energy 17 (11) : 1753 ~ 1766

  16. [학술지] Li, S. / 2015 / Wind Power Forecasting Using Neural Network Ensembles With Feature Selection / IEEE Transactions on Sustainable Energy 6 (4) : 1447 ~ 1456

  17. [학술지] Davò, F. / 2016 / Post-processing Techniques and Principal Component Analysis for Regional Wind Power and Solar Irradiance Forecasting / Solar Energy 134 : 327 ~ 338

  18. [학술지] Wu, Q. / 2016 / Wind Power Generation Forecasting Using Least Squares Support Vector Machine Combined with Ensemble Empirical Mode Decomposition, Principal Component Analysis and a Bat Algorithm / Energies 9 (4) : 261