Minimizing asymmetric loss in medium-term wind power forecasting

2015 ◽  
Vol 81 ◽  
pp. 197-208 ◽  
Author(s):  
Carsten Croonenbroeck ◽  
Georg Stadtmann
Keyword(s):  
Wind Power ◽  
Medium Term ◽  
Asymmetric Loss ◽  
Wind Power Forecasting ◽  
Power Forecasting

Application of grey theory and neural network in medium term wind power forecasting

2017 ◽  
Author(s):  
Xiangxing Meng ◽  
Wanyou Zhao ◽  
Haitao Wang ◽  
Yang Gao ◽  
Hongyu Zhong ◽  
...  
Keyword(s):  
Neural Network ◽  
Wind Power ◽  
Grey Theory ◽  
Medium Term ◽  
Wind Power Forecasting ◽  
Power Forecasting

scholarly journals Decomposed Threshold ARMAX Models for short- to medium-term wind power forecasting

2018 ◽  
Vol 51 (13) ◽  
pp. 49-54 ◽  
Author(s):  
C.E. Robles-Rodriguez ◽  
D. Dochain
Keyword(s):  
Wind Power ◽  
Medium Term ◽  
Wind Power Forecasting ◽  
Power Forecasting

scholarly journals Accurate medium-term wind power forecasting in a censored classification framework

Energy ◽  
2014 ◽  
Vol 73 ◽  
pp. 221-232 ◽  
Author(s):  
Carsten Croonenbroeck ◽  
Christian Møller Dahl
Keyword(s):  
Wind Power ◽  
Medium Term ◽  
Wind Power Forecasting ◽  
Classification Framework ◽  
Power Forecasting

Development and Operational Status of Wind Power Forecasting System

2013 ◽  
Vol 133 (4) ◽  
pp. 366-372 ◽  
Author(s):  
Isao Aoki ◽  
Ryoichi Tanikawa ◽  
Nobuyuki Hayasaki ◽  
Mitsuhiro Matsumoto ◽  
Shigero Enomoto
Keyword(s):  
Wind Power ◽  
Wind Power Forecasting ◽  
Forecasting System ◽  
Power Forecasting

Time Series Model of Wind Power Forecasting Error by using Beta Distribution for Optimal Sizing of Battery Storage

2019 ◽  
Vol 139 (3) ◽  
pp. 212-224
Author(s):  
Xiaowei Dui ◽  
Masakazu Ito ◽  
Yu Fujimoto ◽  
Yasuhiro Hayashi ◽  
Guiping Zhu ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Power ◽  
Beta Distribution ◽  
Time Series Model ◽  
Battery Storage ◽  
Wind Power Forecasting ◽  
Optimal Sizing ◽  
Forecasting Error ◽  
Power Forecasting

Wind Power Forecasting Using Wavelet Transform and General Regression Neural Network for Ontario Electricity Market

2020 ◽  
Vol 13 (1) ◽  
pp. 16-26
Author(s):  
Sumit Saroha ◽  
Sanjeev K. Aggarwal
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Wavelet Transform ◽  
Wind Power ◽  
Electricity Market ◽  
General Regression Neural Network ◽  
Percentage Error ◽  
Wind Power Forecasting ◽  
General Regression ◽  
Power Forecasting

Objective: The estimation accuracy of wind power is an important subject of concern for reliable grid operations and taking part in open access. So, with an objective to improve the wind power forecasting accuracy. Methods: This article presents Wavelet Transform (WT) based General Regression Neural Network (GRNN) with statistical time series input selection technique. Results: The results of the proposed model are compared with four different models namely naïve benchmark model, feed forward neural networks, recurrent neural networks and GRNN on the basis of Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) performance metric. Conclusion: The historical data used by the presented models has been collected from the Ontario Electricity Market for the year 2011 to 2015 and tested for a long time period of more than two years (28 months) from November 2012 to February 2015 with one month estimation moving window.

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