Prediction of Rare Earth Elements in Neutral Alkaline Mine Drainage from Razi Coal Mine, Golestan Province, Northeast Iran, Using General Regression Neural Network

2013 ◽  
Vol 139 (6) ◽  
pp. 896-907 ◽  
Author(s):  
Faramarz Doulati Ardejanii ◽  
Reza Rooki ◽  
Behshad Jodieri Shokri ◽  
Teimour Eslam Kish ◽  
Ahmad Aryafar ◽  
...  
Keyword(s):  
Neural Network ◽  
Rare Earth ◽  
Rare Earth Elements ◽  
Coal Mine ◽  
Mine Drainage ◽  
General Regression Neural Network ◽  
Alkaline Mine Drainage ◽  
Golestan Province ◽  
General Regression ◽  
Northeast Iran

Behavior of rare earth elements in acid coal mine drainage in Shanxi Province, China

2011 ◽  
Vol 67 (1) ◽  
pp. 205-213 ◽  
Author(s):  
Hongfu Sun ◽  
Fenghua Zhao ◽  
Meng Zhang ◽  
Jingqin Li
Keyword(s):  
Rare Earth ◽  
Rare Earth Elements ◽  
Coal Mine ◽  
Mine Drainage ◽  
Shanxi Province ◽  
Coal Mine Drainage

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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