scholarly journals Detonation cell size model based on deep neural network for hydrogen, methane and propane mixtures with air and oxygen

2019 ◽  
Vol 51 (2) ◽  
pp. 424-431 ◽  
Author(s):  
Konrad Malik ◽  
Mateusz Żbikowski ◽  
Andrzej Teodorczyk
2021 ◽  
Vol 12 (10) ◽  
pp. 1015-1024
Author(s):  
Xiaoliang Yang ◽  
Weiping Ni ◽  
Weidong Yan ◽  
Hui Bian ◽  
Han Zhang ◽  
...  

Author(s):  
Hendrik Wohrle ◽  
Mariela De Lucas Alvarez ◽  
Fabian Schlenke ◽  
Alexander Walsemann ◽  
Michael Karagounis ◽  
...  

2019 ◽  
Vol 9 (7) ◽  
pp. 1487 ◽  
Author(s):  
Fei Mei ◽  
Qingliang Wu ◽  
Tian Shi ◽  
Jixiang Lu ◽  
Yi Pan ◽  
...  

Recently, a large number of distributed photovoltaic (PV) power generations have been connected to the power grid, which resulted in an increased fluctuation of the net load. Therefore, load forecasting has become more difficult. Considering the characteristics of the net load, an ultrashort-term forecasting model based on phase space reconstruction and deep neural network (DNN) is proposed, which can be divided into two steps. First, the phase space reconstruction of the net load time series data is performed using the C-C method. Second, the reconstructed data is fitted by the DNN to obtain the predicted value of the net load. The performance of this model is verified using real data. The accuracy is high in forecasting the net load under high PV penetration rate and different weather conditions.


2020 ◽  
Author(s):  
Andrew M. Knisely ◽  
Andrew Naples ◽  
Kyle B. Brady ◽  
John Hoke ◽  
Stephen A. Schumaker

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