Battery aging-aware energy management strategy with dual-state feedback for improving life cycle economy by using multi-neural networks learning algorithm

2022 ◽  
Vol 46 ◽  
pp. 103890
Author(s):  
Xinyou Lin ◽  
Jiajin Zhang
Keyword(s):  
Neural Networks ◽  
Life Cycle ◽  
Energy Management ◽  
Management Strategy ◽  
State Feedback ◽  
Learning Algorithm ◽  
Energy Management Strategy ◽  
Cycle Economy

scholarly journals Integration of artificial neural networks for multi-source energy management in a smart grid

2021 ◽  
Vol 12 (3) ◽  
pp. 1919
Author(s):  
Eziitouni Jarmouni ◽  
Ahmed Mouhsen ◽  
Mohammed Lamhammedi ◽  
Hicham Ouldzira
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Renewable Energy ◽  
Energy Management ◽  
Management Strategy ◽  
Renewable Energy Sources ◽  
Solar Panels ◽  
Energy Management Strategy ◽  
The Public ◽  
Artificial Neural

<span lang="EN-US">Among the most widespread renewable energy sources is solar energy; Solar panels offer a green, clean, and environmentally friendly source of energy. In the presence of several advantages of the use of photovoltaic systems, the random operation of the photovoltaic generator presents a great challenge, in the presence of a critical load. Among the most used solutions to overcome this problem is the combination of solar panels with generators or with the public grid or both. In this paper, an energy management strategy is proposed with a safety aspect by using artificial neural networks (ANNs), in order to ensure a continuous supply of electricity to consumers with a maximum solicitation of renewable energy.</span>

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