해외사례 고찰을 통한 풍력 예측모형의 과거와 현재
Past and Present of Wind Power Prediction Models through Overseas Case Review
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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.
Abstract
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.
참고문헌 (18)
- "국내 풍력, ‘제주·강원’ 가장 많아" (2016), 송명규[URL]
- Bechrakis, D. A., "Simulation of the Wind Speed at Different Heights Using Artificial Neural Networks", Wind Engineering, 24(2), pp.127~136 (2000)
- Sfetsos, A., "A Novel Approach for the Forecasting of Mean Hourly Wind Speed Time Series", Renewable Energy, 27(2), pp.163~174 (2002)
- More, A., "Forecasting Wind with Neural Networks", Marine structures, 16(1), pp.35~49 (2003)
- Campbell, P. R. J., "A Novel Approach to Wind Forecasting in the United Kingdom and Ireland", International Journal Of Simulation Systems, 6(12-13), pp.1~10 (2005)
- Potter, C. W., "Very Short-term Wind Forecasting for Tasmanian Power Generation", IEEE Transactions on Power Systems, 21(2), pp.965~ (2006)
- Barbounis, T. G., "Long-Term Wind Speed and Power Forecasting Using Local Recurrent Neural Network Models", IEEE Transactions on Energy Conversion, 21(1), pp.273~284 (2006)
- Barbounis, T. G., "A Locally Recurrent Fuzzy Neural Network with Application to the Wind Speed Prediction Using Spatial Correlation", Neurocomputing, 70(7), pp.1525~1542 (2007)
- Kavasseri, R. G., "Day-ahead Wind Speed Forecasting Using f-ARIMA Models", Renewable Energy, 34(5), pp.1388~1393 (2009)
- Baile, R., "Short‐Term Forecasting of Surface Layer Wind Speed Using a Continuous Random Cascade Model", Wind Energy, 14(6), pp.719~734 (2011)
- Poitras, G., "Wind Speed Prediction for a Target Station Using Neural Networks and Particle Swarm Optimization", Wind Engineering, 35(3), pp.369~380 (2011)
- Cao, Q., "Forecasting Wind Speed with Recurrent Neural Networks", European Journal of Operational Research, 221(1), pp.148~154 (2012)
- Liu, Z., "Wind Power Plant Prediction by Using Neural Networks", 2012 IEEE Energy Conversion Congress and Exposition, pp.3154~3160 (2012)
- Vladislavleva, E., "Predicting the Energy Output of Wind Farms Based on Weather Data: Important Variables and Their Correlation", Renewable energy, 50, pp.236~243 (2013)
- Messner, J. W., "Probabilistic Wind Power Forecasts with an Inverse Power Curve Transformation and Censored Regression", Wind Energy, 17(11), pp.1753~1766 (2014)
- Li, S., "Wind Power Forecasting Using Neural Network Ensembles With Feature Selection", IEEE Transactions on Sustainable Energy, 6(4), pp.1447~1456 (2015)
- Davò, F., "Post-processing Techniques and Principal Component Analysis for Regional Wind Power and Solar Irradiance Forecasting", Solar Energy, 134, pp.327~338 (2016)
- Wu, Q., "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), pp.261~ (2016)