<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>China is moving forward, but it pays much attention to data analysis of infinite resource circulation. Therefore, this paper investigates the time-domain convolutional calculation method of deep learning and recurrent calculation method of deep learning and uses them to study and analyze the obstacles and countermeasures for the share of renewable energy in our country’s right grid. Then, the passing situation of renewable energy in China is analyzed. Information related to China’s advancement and utilization of inexhaustible resources from 2010 to 2020. From the analysis of the gathered information, it is unequivocal that the scale and share of inexhaustible resources of ancestors in our country’s rights are gradually increasing. Space occupancy in recent 3 years of inexhaustible resources generation in our country will be more than 133 million kilowatts, and the proportion is about 70% of our country’s right power supply; the solar power generation capacity will increase from almost nothing to 261.1 billion kilowatt-hours in 2020 from less than 300 million kilowatt-hours in 2010, which is nearly exponential growth. Finally, this paper combines the existing features of renewable energy in China, puts forward the problems and challenges encountered in the advancement of inexhaustible resources, and gives countermeasures that can be strengthened the utilization rate of renewable energy in the power grid.</p>
</abstract>

China is moving forward, but it pays much attention to data analysis of infinite resource circulation. Therefore, this paper investigates the time-domain convolutional calculation method of deep learning and recurrent calculation method of deep learning and uses them to study and analyze the obstacles and countermeasures for the share of renewable energy in our country’s right grid. Then, the passing situation of renewable energy in China is analyzed. Information related to China’s advancement and utilization of inexhaustible resources from 2010 to 2020. From the analysis of the gathered information, it is unequivocal that the scale and share of inexhaustible resources of ancestors in our country’s rights are gradually increasing. Space occupancy in recent 3 years of inexhaustible resources generation in our country will be more than 133 million kilowatts, and the proportion is about 70% of our country’s right power supply; the solar power generation capacity will increase from almost nothing to 261.1 billion kilowatt-hours in 2020 from less than 300 million kilowatt-hours in 2010, which is nearly exponential growth. Finally, this paper combines the existing features of renewable energy in China, puts forward the problems and challenges encountered in the advancement of inexhaustible resources, and gives countermeasures that can be strengthened the utilization rate of renewable energy in the power grid.

Barriers to Renewable Energy Share of the Grid in the Context of Big Data and Countermeasures

School of Intelligent Manufacturing, Changzhou Vocational Institute of Technology

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

China is moving forward, but it pays much attention to data analysis of infinite resource circulation. Therefore, this paper investigates the time-domain...

Barriers to Renewable Energy Share of the Grid in the Context of Big Data and Countermeasures

Published Online: Aug 07, 2023

Page range: -

Received: Sep 13, 2022

Accepted: Feb 15, 2023

DOI: https://doi.org/10.2478/amns.2023.2.00144

Keywords
Big data, Renewable energy, Power generators, Time-domain convolutional networks, Recurrent neural networks

© 2023 Hao Cai et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Barriers to Renewable Energy Share of the Grid in the Context of Big Data and Countermeasures

Published Online: Aug 07, 2023

Page range: -

Received: Sep 13, 2022

Accepted: Feb 15, 2023

DOI: https://doi.org/10.2478/amns.2023.2.00144

KeywordsBig data, Renewable energy, Power generators, Time-domain convolutional networks, Recurrent neural networks

© 2023 Hao Cai et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Big data, Renewable energy, Power generators, Time-domain convolutional networks, Recurrent neural networks