scholarly journals Optimization of the Conceptual Model of Green-Ampt Using Artificial Neural Network Model (ANN) and WMS to Estimate Infiltration Rate of Soil (Case Study: Kakasharaf Watershed, Khorram Abad, Iran)

2014 ◽  
Vol 06 (05) ◽  
pp. 473-480 ◽  
Author(s):  
Ali Haghizadeh ◽  
Leila Soleimani ◽  
Hossein Zeinivand
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Conceptual Model ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Infiltration Rate ◽  
Artificial Neural

Artificial Neural Network Model for Rainfall-Runoff -A Case Study

2016 ◽  
Vol 9 (3) ◽  
pp. 263-272 ◽  
Author(s):  
P.Sundara Kumar ◽  
T. V. Praveen ◽  
M. Anjanaya Prasad
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Rainfall Runoff ◽  
Artificial Neural

scholarly journals Prediction of groundwater suitability for irrigation using artificial neural network model: a case study of Nanded tehsil, Maharashtra, India

2016 ◽  
Vol 2 (4) ◽  
pp. 1-10 ◽  
Author(s):  
Vasant Madhav Wagh ◽  
Dipak Baburao Panaskar ◽  
Aniket Avinash Muley ◽  
Shrikant Vitthal Mukate ◽  
Yogesh Popatrao Lolage ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Groundwater Suitability ◽  
Artificial Neural

scholarly journals Artificial neural network model for air pollution prediction: case study of Moravica District, Serbia

2018 ◽  
Vol 44 (1) ◽  
Author(s):  
Marija Blagojević ◽  
Miloš Papić ◽  
Momčilo Vujičić ◽  
Marko Šućurović
Keyword(s):  
Neural Network ◽  
Air Pollution ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Artificial Neural ◽  
Air Pollution Prediction ◽  
Pollution Prediction

scholarly journals Exploiting Artificial Neural Networks for the Prediction of Ancillary Energy Market Prices

Energies ◽  
2018 ◽  
Vol 11 (7) ◽  
pp. 1906 ◽  
Author(s):  
Christian Giovanelli ◽  
Seppo Sierla ◽  
Ryutaro Ichise ◽  
Valeriy Vyatkin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Energy Market ◽  
Market Prices ◽  
Artificial Neural

The increase of distributed energy resources in the smart grid calls for new ways to profitably exploit these resources, which can participate in day-ahead ancillary energy markets by providing flexibility. Higher profits are available for resource owners that are able to anticipate price peaks and hours of low prices or zero prices, as well as to control the resource in such a way that exploits the price fluctuations. Thus, this study presents a solution in which artificial neural networks are exploited to predict the day-ahead ancillary energy market prices. The study employs the frequency containment reserve for the normal operations market as a case study and presents the methodology utilized for the prediction of the case study ancillary market prices. The relevant data sources for predicting the market prices are identified, then the frequency containment reserve market prices are analyzed and compared with the spot market prices. In addition, the methodology describes the choices behind the definition of the model validation method and the performance evaluation coefficient utilized in the study. Moreover, the empirical processes for designing an artificial neural network model are presented. The performance of the artificial neural network model is evaluated in detail by means of several experiments, showing robustness and adaptiveness to the fast-changing price behaviors. Finally, the developed artificial neural network model is shown to have better performance than two state of the art models, support vector regression and ARIMA, respectively.

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