An advanced approach for optimal wind power generation prediction intervals by using self-adaptive evolutionary extreme learning machine

2018 ◽  
Vol 126 ◽  
pp. 254-269 ◽  
Author(s):  
Tawfek Mahmoud ◽  
Z.Y. Dong ◽  
Jin Ma
Keyword(s):  
Power Generation ◽  
Wind Power ◽  
Extreme Learning Machine ◽  
Wind Power Generation ◽  
Prediction Intervals ◽  
Learning Machine ◽  
Self Adaptive

scholarly journals Probabilistic Forecasting of Wind Power Generation Using Extreme Learning Machine

2014 ◽  
Vol 29 (3) ◽  
pp. 1033-1044 ◽  
Author(s):  
Can Wan ◽  
Zhao Xu ◽  
Pierre Pinson ◽  
Zhao Yang Dong ◽  
Kit Po Wong
Keyword(s):  
Power Generation ◽  
Wind Power ◽  
Extreme Learning Machine ◽  
Wind Power Generation ◽  
Probabilistic Forecasting ◽  
Learning Machine

scholarly journals Forecasting model of power generated by wind power plants

2021 ◽  
Vol 926 (1) ◽  
pp. 012084
Author(s):  
A M Ilyas ◽  
A Suyuti ◽  
I C Gunadin ◽  
S M Said
Keyword(s):  
Power Generation ◽  
Power Plant ◽  
Wind Power ◽  
Extreme Learning Machine ◽  
Power Plants ◽  
Percentage Error ◽  
Power Balance ◽  
Wind Power Plant ◽  
Wind Power Plants ◽  
Learning Machine

Abstract The power generated by wind power plants is unstable so forecasting is needed to maintain the power balance in an interconnected system. The purpose of this research is to predict the power generated at the Sidrap and Jeneponto wind power plants. The method used is an optimally pruned extreme learning machine (OPELM). The extreme learning machine (ELM) method is used as a comparison method. The mean absolute percentage error (MAPE) method is used to assess the level of forecasting accuracy. Forecasting power generation with Sidrap wind power plant data using the OPELM method is 0.8970% more accurate than the ELM which is 1.0853%. In general, the OPELM method is more accurate. Forecasting power generation with data from the Jeneponto wind power plant using the OPELM method is 2.4887% more accurate than the ELM method is 2.9984%. These results indicate that linear, sigmoid, and Gaussian activation in the OPELM method can increase accuracy. The OPELM method can be tested in forecasting the power generation at the Sidrap and Jeneponto wind power plants to maintain a power balance in the Sulselbar power grid system.

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