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http://dx.doi.org/10.5351/KJAS.2021.34.5.723

Wind power forecasting based on time series and machine learning models  

Park, Sujin (Department of Applied Statistics, University of Chung-Ang)
Lee, Jin-Young (Department of Applied Statistics, University of Chung-Ang)
Kim, Sahm (Department of Applied Statistics, University of Chung-Ang)
Publication Information
The Korean Journal of Applied Statistics / v.34, no.5, 2021 , pp. 723-734 More about this Journal
Abstract
Wind energy is one of the rapidly developing renewable energies which is being developed and invested in response to climate change. As renewable energy policies and power plant installations are promoted, the supply of wind power in Korea is gradually expanding and attempts to accurately predict demand are expanding. In this paper, the ARIMA and ARIMAX models which are Time series techniques and the SVR, Random Forest and XGBoost models which are machine learning models were compared and analyzed to predict wind power generation in the Jeonnam and Gyeongbuk regions. Mean absolute error (MAE) and mean absolute percentage error (MAPE) were used as indicators to compare the predicted results of the model. After subtracting the hourly raw data from January 1, 2018 to October 24, 2020, the model was trained to predict wind power generation for 168 hours from October 25, 2020 to October 31, 2020. As a result of comparing the predictive power of the models, the Random Forest and XGBoost models showed the best performance in the order of Jeonnam and Gyeongbuk. In future research, we will try not only machine learning models but also forecasting wind power generation based on data mining techniques that have been actively researched recently.
Keywords
time series; machine learning; wind power forecasting; random forest; XGBoost;
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