High resolution photovoltaic power generation potential assessments of rooftop in China. (November 2022)
- Record Type:
- Journal Article
- Title:
- High resolution photovoltaic power generation potential assessments of rooftop in China. (November 2022)
- Main Title:
- High resolution photovoltaic power generation potential assessments of rooftop in China
- Authors:
- Wang, Lichao
Xu, Shengzhi
Gong, Youkang
Ning, Jing
Zhang, Xiaodan
Zhao, Ying - Abstract:
- Abstract: Rooftop photovoltaic system plays an important role in solar energy power generation especially in urban. In this paper, we present an assessment method for the PV power generation potential of rooftop in China. Using machine learning model processes the big data that consists of the gross domestic product, building footprint, road length and population, at a high geographic resolution of 10 km by 10 km. The result shows that the rooftop generation potential in China is 3.27×10 9 MWh annually, which is close to half of the total electricity generation of China mainland in 2020, and will contribute to 2.41×10 9 tons of CO2 emission reduction per year. The regional results are counted by provinces and cities, showing Shandong province is the one with highest potential of 0.275×10 9 MWh. On the whole, the western region covers a large area with sufficient solar radiation, while the eastern region has greater photovoltaic power generation potential because of its available roof resources. The development potential of first tier, new first tier and second tier cities is generally greater than that of third and fourth tier cities. Highlights: A high-resolution solar photovoltaic potential map of China utilizes the open dataset and one novel neural network model. The data are stated by provinces and cities showing the regional differences. The rooftop photovoltaic generation will be closed to half of the electricity generation of China mainland in 2020. The eastern regionAbstract: Rooftop photovoltaic system plays an important role in solar energy power generation especially in urban. In this paper, we present an assessment method for the PV power generation potential of rooftop in China. Using machine learning model processes the big data that consists of the gross domestic product, building footprint, road length and population, at a high geographic resolution of 10 km by 10 km. The result shows that the rooftop generation potential in China is 3.27×10 9 MWh annually, which is close to half of the total electricity generation of China mainland in 2020, and will contribute to 2.41×10 9 tons of CO2 emission reduction per year. The regional results are counted by provinces and cities, showing Shandong province is the one with highest potential of 0.275×10 9 MWh. On the whole, the western region covers a large area with sufficient solar radiation, while the eastern region has greater photovoltaic power generation potential because of its available roof resources. The development potential of first tier, new first tier and second tier cities is generally greater than that of third and fourth tier cities. Highlights: A high-resolution solar photovoltaic potential map of China utilizes the open dataset and one novel neural network model. The data are stated by provinces and cities showing the regional differences. The rooftop photovoltaic generation will be closed to half of the electricity generation of China mainland in 2020. The eastern region has great accumulated photovoltaic electricity potential, which is 3.21 times that of the western region. … (more)
- Is Part Of:
- Energy reports. Volume 8(2022)
- Journal:
- Energy reports
- Issue:
- Volume 8(2022)
- Issue Display:
- Volume 8, Issue 2022 (2022)
- Year:
- 2022
- Volume:
- 8
- Issue:
- 2022
- Issue Sort Value:
- 2022-0008-2022-0000
- Page Start:
- 14545
- Page End:
- 14553
- Publication Date:
- 2022-11
- Subjects:
- Rooftop photovoltaic -- Geographic data -- Machine learning -- Photovoltaic potential -- CO2 emission reduction
Power resources -- Periodicals
Energy industries -- Periodicals
Power resources
Periodicals
Electronic journals
621.04205 - Journal URLs:
- http://www.sciencedirect.com/science/journal/23524847/ ↗
http://www.sciencedirect.com/ ↗ - DOI:
- 10.1016/j.egyr.2022.10.396 ↗
- Languages:
- English
- ISSNs:
- 2352-4847
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - BLDSS-3PM
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