DocumentCode :
3475862
Title :
Prediction of wind power generation and power ramp rate with time series analysis
Author :
Hwang, Mi Yeong ; Jin, Cheng Hao ; Lee, Yang Koo ; Kim, Kwang Deuk ; Shin, Jung Hoon ; Ryu, Keun Ho
Author_Institution :
Database/Bioinformactics Lab., Chungbuk Nat. Univ., Cheongju, South Korea
fYear :
2011
fDate :
27-30 Sept. 2011
Firstpage :
512
Lastpage :
515
Abstract :
The use of fossil fuel in the world has been increasing and it generates lots of greenhouse gases. As a result, environmental pollution brought us a serious weather change. In order to reduce the environmental pollution, we should use renewable energy that does not produce any pollution such as wind data. However, wind data can change much in a short time, which is called ramp event. It can make the demand and response imbalance and also cause damages to the wind turbines. Therefore, we should predict the power generation and power ramp rate (PRR) to avoid these problems. In this paper, we predicted the wind power generation and PRR with exponential smoothing method and ARIMA. The prediction method predict wind power generation and PRR after 1 minute using data measured 1 hour ago at 10 intervals. We got forecasting error rate such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE), and then we compared two results of ARIMA and exponential smoothing method. The comparison results showed that exponential smoothing method gets better prediction accuracy than ARIMA.
Keywords :
air pollution control; autoregressive moving average processes; mean square error methods; smoothing methods; time series; wind power plants; wind turbines; ARIMA; MAE; RMSE; autoregressive integrated moving average; environmental pollution reduction; exponential smoothing method; forecasting error rate; fossil fuel; greenhouse gases; mean absolute error; power ramp rate prediction; ramp event; renewable energy; root mean square error; time 1 hour; time 1 min; time series analysis; wind data; wind power generation prediction; wind turbines; Databases; Forecasting; Gold; Green products; Measurement uncertainty; Pollution measurement; ARIMA; exponential smoothing method; power ramp rate; univariate time series model; wind power generation;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Awareness Science and Technology (iCAST), 2011 3rd International Conference on
Conference_Location :
Dalian
Print_ISBN :
978-1-4577-0887-9
Type :
conf
DOI :
10.1109/ICAwST.2011.6163182
Filename :
6163182
Link To Document :
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