scholarly journals Assessment of critical parameters for artificial neural networks based short-term wind generation forecasting

2020 ◽  
Vol 161 ◽  
pp. 878-892
Author(s):  
V.N. Sewdien ◽  
R. Preece ◽  
J.L. Rueda Torres ◽  
E. Rakhshani ◽  
M. van der Meijden
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Critical Parameters ◽  
Wind Generation ◽  
Short Term ◽  
Artificial Neural

Short-term fading simulation using artificial neural networks

2011 ◽  
Vol 65 (7) ◽  
pp. 641-649 ◽  
Author(s):  
Nikola Tomasevic ◽  
Aleksandar Neskovic ◽  
Natasa Neskovic
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Short Term ◽  
Artificial Neural

scholarly journals Prediction of the Fundamental Period of Infilled RC Frame Structures Using Artificial Neural Networks

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Panagiotis G. Asteris ◽  
Athanasios K. Tsaris ◽  
Liborio Cavaleri ◽  
Constantinos C. Repapis ◽  
Angeliki Papalou ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Fundamental Period ◽  
Critical Parameters ◽  
Frame Structures ◽  
Rc Frame ◽  
Infill Walls ◽  
Rc Structures ◽  
Artificial Neural ◽  
Rc Frame Structures

The fundamental period is one of the most critical parameters for the seismic design of structures. There are several literature approaches for its estimation which often conflict with each other, making their use questionable. Furthermore, the majority of these approaches do not take into account the presence of infill walls into the structure despite the fact that infill walls increase the stiffness and mass of structure leading to significant changes in the fundamental period. In the present paper, artificial neural networks (ANNs) are used to predict the fundamental period of infilled reinforced concrete (RC) structures. For the training and the validation of the ANN, a large data set is used based on a detailed investigation of the parameters that affect the fundamental period of RC structures. The comparison of the predicted values with analytical ones indicates the potential of using ANNs for the prediction of the fundamental period of infilled RC frame structures taking into account the crucial parameters that influence its value.

Innovative hybrid models for forecasting time series applied in wind generation based on the combination of time series models with artificial neural networks

Energy ◽  
2018 ◽  
Vol 151 ◽  
pp. 347-357 ◽  
Author(s):  
Henrique do Nascimento Camelo ◽  
Paulo Sérgio Lucio ◽  
João Bosco Verçosa Leal Junior ◽  
Paulo Cesar Marques de Carvalho ◽  
Daniel von Glehn dos Santos
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Hybrid Models ◽  
Wind Generation ◽  
Time Series Models ◽  
Artificial Neural
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