Barriers to Renewable Energy Share of the Grid in the Context of Big Data and Countermeasures
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 obstacl...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2024-01-01
|
| Series: | Applied Mathematics and Nonlinear Sciences |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/amns.2023.2.00144 |
| _version_ | 1797340791706746880 |
|---|---|
| author | Cai Hao Tang Jing Feng Fei |
| author_facet | Cai Hao Tang Jing Feng Fei |
| author_sort | Cai Hao |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T10:08:28Z |
| format | Article |
| id | doaj.art-1c7d2f56fada4aaa9bb94a02bb49b602 |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-03-08T10:08:28Z |
| publishDate | 2024-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-1c7d2f56fada4aaa9bb94a02bb49b6022024-01-29T08:52:29ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562024-01-019110.2478/amns.2023.2.00144Barriers to Renewable Energy Share of the Grid in the Context of Big Data and CountermeasuresCai Hao0Tang Jing1Feng Fei2School of Intelligent Manufacturing, Changzhou Vocational Institute of Technology, Changzhou, Jiangsu, 213164, ChinaSchool of Intelligent Manufacturing, Changzhou Vocational Institute of Technology, Changzhou, Jiangsu, 213164, ChinaSchool of Intelligent Manufacturing, Changzhou Vocational Institute of Technology, Changzhou, Jiangsu, 213164, ChinaChina 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.https://doi.org/10.2478/amns.2023.2.00144big datarenewable energypower generatorstime-domain convolutional networksrecurrent neural networks62-07 |
| spellingShingle | Cai Hao Tang Jing Feng Fei Barriers to Renewable Energy Share of the Grid in the Context of Big Data and Countermeasures Applied Mathematics and Nonlinear Sciences big data renewable energy power generators time-domain convolutional networks recurrent neural networks 62-07 |
| title | Barriers to Renewable Energy Share of the Grid in the Context of Big Data and Countermeasures |
| title_full | Barriers to Renewable Energy Share of the Grid in the Context of Big Data and Countermeasures |
| title_fullStr | Barriers to Renewable Energy Share of the Grid in the Context of Big Data and Countermeasures |
| title_full_unstemmed | Barriers to Renewable Energy Share of the Grid in the Context of Big Data and Countermeasures |
| title_short | Barriers to Renewable Energy Share of the Grid in the Context of Big Data and Countermeasures |
| title_sort | barriers to renewable energy share of the grid in the context of big data and countermeasures |
| topic | big data renewable energy power generators time-domain convolutional networks recurrent neural networks 62-07 |
| url | https://doi.org/10.2478/amns.2023.2.00144 |
| work_keys_str_mv | AT caihao barrierstorenewableenergyshareofthegridinthecontextofbigdataandcountermeasures AT tangjing barrierstorenewableenergyshareofthegridinthecontextofbigdataandcountermeasures AT fengfei barrierstorenewableenergyshareofthegridinthecontextofbigdataandcountermeasures |