Analysis of Rooftop Photovoltaic Potential and Electricity Planning in Lanzhou Urban Areas
With the rapid development of science and technology, the global demand for renewable energy is increasing. In the urban context, solar energy has become one of the key ways to increase urban energy self-sufficiency and reduce carbon emissions due to its flexibility in installation and ease of expan...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Buildings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-5309/15/13/2207 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849320896637960192 |
|---|---|
| author | Yifu Chen Shidong Wang Tao Li |
| author_facet | Yifu Chen Shidong Wang Tao Li |
| author_sort | Yifu Chen |
| collection | DOAJ |
| description | With the rapid development of science and technology, the global demand for renewable energy is increasing. In the urban context, solar energy has become one of the key ways to increase urban energy self-sufficiency and reduce carbon emissions due to its flexibility in installation and ease of expansion of applications. Therefore, based on Geographic Information System (GIS) and deep learning modeling, this paper proposes a method to efficiently assess the potential of urban rooftop solar photovoltaic (PV), which is analyzed in a typical area of Lanzhou New District, which is divided into 8774 units with an area of 87.74 km<sup>2</sup>. The results show that the method has a high accuracy for the identification of the roof area, with a maximum <i>maxF<sub>β</sub></i> of 0.889. The annual solar PV potential of industrial and residential buildings reached 293.602 GWh and 223.198 GWh, respectively, by using the PV panel simulation filling method for the calculation of the area of roofs where the PV panels can be installed. Furthermore, the rooftop PV potential of the industrial buildings in the research area provided can cover 75.17% of the industrial electricity consumption. This approach can provide scientific guidance and data support for regional solar PV planning, which should prioritize the development of solar potential of industrial buildings in the actual consideration of rooftop PV deployment planning. |
| format | Article |
| id | doaj-art-daf507f0a12d4a1d878aaabf384de5fc |
| institution | Kabale University |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Buildings |
| spelling | doaj-art-daf507f0a12d4a1d878aaabf384de5fc2025-08-20T03:49:55ZengMDPI AGBuildings2075-53092025-06-011513220710.3390/buildings15132207Analysis of Rooftop Photovoltaic Potential and Electricity Planning in Lanzhou Urban AreasYifu Chen0Shidong Wang1Tao Li2Gansu Engineering Consulting Group Co., Ltd., Lanzhou 730030, ChinaGansu Institute of Architectural Design and Research Co., Ltd., Lanzhou 730000, ChinaSchool of Urban Construction, Wuhan University of Science and Technology, Wuhan 430065, ChinaWith the rapid development of science and technology, the global demand for renewable energy is increasing. In the urban context, solar energy has become one of the key ways to increase urban energy self-sufficiency and reduce carbon emissions due to its flexibility in installation and ease of expansion of applications. Therefore, based on Geographic Information System (GIS) and deep learning modeling, this paper proposes a method to efficiently assess the potential of urban rooftop solar photovoltaic (PV), which is analyzed in a typical area of Lanzhou New District, which is divided into 8774 units with an area of 87.74 km<sup>2</sup>. The results show that the method has a high accuracy for the identification of the roof area, with a maximum <i>maxF<sub>β</sub></i> of 0.889. The annual solar PV potential of industrial and residential buildings reached 293.602 GWh and 223.198 GWh, respectively, by using the PV panel simulation filling method for the calculation of the area of roofs where the PV panels can be installed. Furthermore, the rooftop PV potential of the industrial buildings in the research area provided can cover 75.17% of the industrial electricity consumption. This approach can provide scientific guidance and data support for regional solar PV planning, which should prioritize the development of solar potential of industrial buildings in the actual consideration of rooftop PV deployment planning.https://www.mdpi.com/2075-5309/15/13/2207deep learningrooftop solar potentialphotovoltaic applicationsregional electricity planning |
| spellingShingle | Yifu Chen Shidong Wang Tao Li Analysis of Rooftop Photovoltaic Potential and Electricity Planning in Lanzhou Urban Areas Buildings deep learning rooftop solar potential photovoltaic applications regional electricity planning |
| title | Analysis of Rooftop Photovoltaic Potential and Electricity Planning in Lanzhou Urban Areas |
| title_full | Analysis of Rooftop Photovoltaic Potential and Electricity Planning in Lanzhou Urban Areas |
| title_fullStr | Analysis of Rooftop Photovoltaic Potential and Electricity Planning in Lanzhou Urban Areas |
| title_full_unstemmed | Analysis of Rooftop Photovoltaic Potential and Electricity Planning in Lanzhou Urban Areas |
| title_short | Analysis of Rooftop Photovoltaic Potential and Electricity Planning in Lanzhou Urban Areas |
| title_sort | analysis of rooftop photovoltaic potential and electricity planning in lanzhou urban areas |
| topic | deep learning rooftop solar potential photovoltaic applications regional electricity planning |
| url | https://www.mdpi.com/2075-5309/15/13/2207 |
| work_keys_str_mv | AT yifuchen analysisofrooftopphotovoltaicpotentialandelectricityplanninginlanzhouurbanareas AT shidongwang analysisofrooftopphotovoltaicpotentialandelectricityplanninginlanzhouurbanareas AT taoli analysisofrooftopphotovoltaicpotentialandelectricityplanninginlanzhouurbanareas |