Deep learning method for evaluating photovoltaic potential of urban land-use: A case study of Wuhan, China
•New method for evaluating rooftop PV potential in land-use type.•Decline the difficulty of obtaining urban land-use data.•Capability of analyzing several cities with less time cost.•Three types of land use have the highest rooftop solar utilization potential. To strengthen the synergy between urban...
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| Published in | Applied energy Vol. 283; p. 116329 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.02.2021
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| Subjects | |
| Online Access | Get full text |
| ISSN | 0306-2619 1872-9118 |
| DOI | 10.1016/j.apenergy.2020.116329 |
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| Abstract | •New method for evaluating rooftop PV potential in land-use type.•Decline the difficulty of obtaining urban land-use data.•Capability of analyzing several cities with less time cost.•Three types of land use have the highest rooftop solar utilization potential.
To strengthen the synergy between urban photovoltaic development and urban planning, which can help to promote photovoltaic and renewable energy development in cities, a workflow based on a deep-learning method by using neural networks and urban satellite images is constructed, which is applied to study the relationship between urban rooftop photovoltaic potential and urban land use. A Chinese city, Wuhan, has been considered the subject and divided into 5184 units, which is a 1201.2 km2 area in central urban space. The result shows that, three types of urban land use type have the highest rooftop photovoltaic potential, which are Continuous Urban area, Discontinuous Dense Urban area and Industrial, commercial, public and education unit. The annual photovoltaic potential of these three land use types have reached 1818.41 GWh/year, 1957.32 GWh/year and 2022.71 GWh/year, and the average photovoltaic power generation per unit area of these three types have reached 11.23 GWh/km2·year, 9.99 GWh/km2·year and 13.07 GWh/km2·year. In addition, these three land use types contributed 71.4% of the city’s total rooftop photovoltaic potential. When considering the coordination of roof photovoltaic development and urban planning, these three types of land use should be given priority. The method and findings can provide urban planning authorities with tools and data to reference when developing urban master plan and PV development plans. |
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| AbstractList | To strengthen the synergy between urban photovoltaic development and urban planning, which can help to promote photovoltaic and renewable energy development in cities, a workflow based on a deep-learning method by using neural networks and urban satellite images is constructed, which is applied to study the relationship between urban rooftop photovoltaic potential and urban land use. A Chinese city, Wuhan, has been considered the subject and divided into 5184 units, which is a 1201.2 km² area in central urban space. The result shows that, three types of urban land use type have the highest rooftop photovoltaic potential, which are Continuous Urban area, Discontinuous Dense Urban area and Industrial, commercial, public and education unit. The annual photovoltaic potential of these three land use types have reached 1818.41 GWh/year, 1957.32 GWh/year and 2022.71 GWh/year, and the average photovoltaic power generation per unit area of these three types have reached 11.23 GWh/km²·year, 9.99 GWh/km2·year and 13.07 GWh/km²·year. In addition, these three land use types contributed 71.4% of the city’s total rooftop photovoltaic potential. When considering the coordination of roof photovoltaic development and urban planning, these three types of land use should be given priority. The method and findings can provide urban planning authorities with tools and data to reference when developing urban master plan and PV development plans. •New method for evaluating rooftop PV potential in land-use type.•Decline the difficulty of obtaining urban land-use data.•Capability of analyzing several cities with less time cost.•Three types of land use have the highest rooftop solar utilization potential. To strengthen the synergy between urban photovoltaic development and urban planning, which can help to promote photovoltaic and renewable energy development in cities, a workflow based on a deep-learning method by using neural networks and urban satellite images is constructed, which is applied to study the relationship between urban rooftop photovoltaic potential and urban land use. A Chinese city, Wuhan, has been considered the subject and divided into 5184 units, which is a 1201.2 km2 area in central urban space. The result shows that, three types of urban land use type have the highest rooftop photovoltaic potential, which are Continuous Urban area, Discontinuous Dense Urban area and Industrial, commercial, public and education unit. The annual photovoltaic potential of these three land use types have reached 1818.41 GWh/year, 1957.32 GWh/year and 2022.71 GWh/year, and the average photovoltaic power generation per unit area of these three types have reached 11.23 GWh/km2·year, 9.99 GWh/km2·year and 13.07 GWh/km2·year. In addition, these three land use types contributed 71.4% of the city’s total rooftop photovoltaic potential. When considering the coordination of roof photovoltaic development and urban planning, these three types of land use should be given priority. The method and findings can provide urban planning authorities with tools and data to reference when developing urban master plan and PV development plans. |
| ArticleNumber | 116329 |
| Author | Xu, Shen Zhang, Chen Jiang, Haihua Li, Zhixin Luo, Yongqiang |
| Author_xml | – sequence: 1 givenname: Chen surname: Zhang fullname: Zhang, Chen email: m201873401@hust.edu.cn organization: School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan, China – sequence: 2 givenname: Zhixin surname: Li fullname: Li, Zhixin email: m201873400@hust.edu.cn organization: School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan, China – sequence: 3 givenname: Haihua surname: Jiang fullname: Jiang, Haihua email: m201873399@hust.edu.cn organization: School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan, China – sequence: 4 givenname: Yongqiang surname: Luo fullname: Luo, Yongqiang email: luoroger@yeah.net organization: School of Environmental Science and Engineering, Huazhong University of Science and Technology, Wuhan, China – sequence: 5 givenname: Shen surname: Xu fullname: Xu, Shen email: xushen@hust.edu.cn organization: School of Architecture and Urban Planning, Huazhong University of Science and Technology, Wuhan, China |
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| Cites_doi | 10.1016/j.solener.2015.03.016 10.1016/j.solener.2008.03.007 10.1016/j.enpol.2013.03.002 10.1016/j.apenergy.2020.115786 10.1016/j.renene.2016.07.003 10.1016/j.rser.2017.10.019 10.1016/j.egyr.2020.09.010 10.1016/j.compenvurbsys.2010.01.001 10.1016/j.rser.2019.109309 10.1145/3065386 10.1016/j.rser.2019.03.041 10.1016/j.apenergy.2018.09.176 10.1016/j.jobe.2020.101528 10.1016/j.solener.2011.02.022 10.1016/j.neucom.2017.01.018 10.1016/j.uclim.2020.100624 10.1016/j.apenergy.2012.08.042 10.1016/j.biosystemseng.2016.08.024 10.1016/j.procs.2020.04.180 10.1016/j.renene.2014.06.028 10.1016/j.enbuild.2013.03.033 10.1016/j.solener.2016.05.061 10.1016/j.isprsjprs.2020.05.022 10.1016/j.rse.2015.12.055 10.1016/j.apenergy.2018.02.008 10.1088/1748-9326/8/1/014042 10.1145/3097983.3098070 10.1016/j.rser.2010.01.001 10.1016/j.renene.2016.02.053 10.1016/j.egypro.2017.09.605 10.1016/j.renene.2017.03.085 10.1016/j.renene.2018.06.087 10.1016/j.enpol.2011.09.054 10.1016/j.rser.2017.05.208 10.1016/j.rse.2019.04.014 10.1016/j.energy.2020.117038 |
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| References | Simon, Uma (b0250) 2020; 171 Lin, Lu, Li (b0050) 2017 Google. Project Sunroof. Google Proj Sunroof; 2016. Development P. Solar Urban Planning Berlin 2004. Sarralde, Quinn, Wiesmann, Steemers (b0070) 2015 Zhang, Xu, Shabunko, Tay, Sun, Lau (b0065) 2019 Poon, Kämpf, Tay, Wong, Reindl (b0120) 2020 Zhou, Ming, Lv, Zhou, Bao, Hong (b0150) 2020 Mohajeri, Upadhyay, Gudmundsson, Assouline, Kämpf, Scartezzini (b0060) 2016 Krizhevsky, Sutskever, Hinton (b0095) 2017 Wegertseder, Lund, Mikkola, García (b0220) 2016 Wiginton, Nguyen, Pearce (b0205) 2010; 34 Yeo, Yoon, Yee (b0015) 2013 Meirich (b0135) 2008 León-Vargas, García-Jaramillo, Krejci (b0045) 2019 Srivastava, Vargas-Muñoz, Tuia (b0245) 2019 Brito, Freitas, Guimarães, Catita, Redweik (b0130) 2017 Karteris, Slini, Papadopoulos (b0115) 2013 European Environment Agency. Copernicus Land Monitoring Service - Urban Atlas; 2017. Singh, Banerjee (b0085) 2015 Wuhan Municipal Statistics Beaural (b0240) 2018 Shaukat, Ali, Mehmood, Khan, Jawad, Farid (b0265) 2018 . Phap, Thu Huong, Hanh, Van Duy, Van Binh (b0255) 2020 Cheng, Zhang, Li, Mao, Xu, Ju (b0125) 2020 Wang, Sun, Wei, Luo, Yang, Xu (b0005) 2018 Yue, Huang (b0080) 2011 Pan, Guan, Chen, Yu, Nunes Gonçalves, Marcato Junior (b0110) 2020 Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. In: 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc.; 2015. Albert, Kaur, Gonzalez (b0160) 2017 Fisher, Flood, Danaher (b0105) 2016 2011. Lukač, Žlaus, Seme, Žalik, Štumberger (b0030) 2013 Google. Overview|Maps Static API | Google Maps Platform 2020. Zhang, Sargent, Pan, Li, Gardiner, Hare (b0100) 2019 Bódis, Kougias, Jäger-Waldau, Taylor, Szabó (b0230) 2019 Yang, Campana, Stridh, Yan (b0090) 2020; 279 International Energy Agency IEA. Potential for building integrated photovoltaics. IEA-PVPS Task; 2002. Izquierdo, Rodrigues, Fueyo (b0195) 2008; 82 Ordóñez, Jadraque, Alegre, Martínez (b0200) 2010 Stoll, Smith, Deinert (b0215) 2013 Buffat, Grassi, Raubal (b0035) 2018 Maruf MH, Haq MA ul, Dey SK, Al Mansur A, Shihavuddin ASM. Adaptation for sustainable implementation of Smart Grid in developing countries like Bangladesh. Energy Reports 2020. Huang, Roland-Holst, Wang, Cai (b0010) 2020 Mehdipour Ghazi, Yanikoglu, Aptoula (b0155) 2017 Dyrmann, Karstoft, Midtiby (b0170) 2016 Bergamasco, Asinari (b0210) 2011; 85 Sun Y wei, Hof A, Wang R, Liu J, Lin Y jie, Yang D wei. GIS-based approach for potential analysis of solar PV generation at the regional scale: a case study of Fujian Province. Energy Policy 2013. Wong, Zhu, Liu, Lu, Peng, Tang (b0235) 2016 Majeed Butt, Zulqarnain, Majeed (b0260) 2020 ESMAP, SOLARGIS, WB, IFC. Global Solar Atlas. Glob Sol Atlas; 2019. Wuhan Natural Resources and Planning Bureau. Wuhan Urban Master Plan (2010-2020). Wuhan, Hubei Province, China: Wuhan Natural Resources and Planning Bureau Ali, Shafiullah, Urmee (b0225) 2018 Lee, Hong, Jeong, Kim (b0055) 2018 Huang, Mendis, Xu (b0180) 2019 Lobaccaro, Croce, Lindkvist, Munari Probst, Scognamiglio, Dahlberg (b0075) 2019 Bergamasco (10.1016/j.apenergy.2020.116329_b0210) 2011; 85 Phap (10.1016/j.apenergy.2020.116329_b0255) 2020 Srivastava (10.1016/j.apenergy.2020.116329_b0245) 2019 Huang (10.1016/j.apenergy.2020.116329_b0010) 2020 10.1016/j.apenergy.2020.116329_b0165 Wong (10.1016/j.apenergy.2020.116329_b0235) 2016 Shaukat (10.1016/j.apenergy.2020.116329_b0265) 2018 Lin (10.1016/j.apenergy.2020.116329_b0050) 2017 Dyrmann (10.1016/j.apenergy.2020.116329_b0170) 2016 Wegertseder (10.1016/j.apenergy.2020.116329_b0220) 2016 Zhang (10.1016/j.apenergy.2020.116329_b0100) 2019 Zhang (10.1016/j.apenergy.2020.116329_b0065) 2019 Buffat (10.1016/j.apenergy.2020.116329_b0035) 2018 Wuhan Municipal Statistics Beaural (10.1016/j.apenergy.2020.116329_b0240) 2018 Sarralde (10.1016/j.apenergy.2020.116329_b0070) 2015 10.1016/j.apenergy.2020.116329_b0040 Fisher (10.1016/j.apenergy.2020.116329_b0105) 2016 Singh (10.1016/j.apenergy.2020.116329_b0085) 2015 Wiginton (10.1016/j.apenergy.2020.116329_b0205) 2010; 34 Huang (10.1016/j.apenergy.2020.116329_b0180) 2019 Zhou (10.1016/j.apenergy.2020.116329_b0150) 2020 Simon (10.1016/j.apenergy.2020.116329_b0250) 2020; 171 Cheng (10.1016/j.apenergy.2020.116329_b0125) 2020 Pan (10.1016/j.apenergy.2020.116329_b0110) 2020 10.1016/j.apenergy.2020.116329_b0190 10.1016/j.apenergy.2020.116329_b0270 Brito (10.1016/j.apenergy.2020.116329_b0130) 2017 Yeo (10.1016/j.apenergy.2020.116329_b0015) 2013 10.1016/j.apenergy.2020.116329_b0020 10.1016/j.apenergy.2020.116329_b0185 Majeed Butt (10.1016/j.apenergy.2020.116329_b0260) 2020 Lee (10.1016/j.apenergy.2020.116329_b0055) 2018 Izquierdo (10.1016/j.apenergy.2020.116329_b0195) 2008; 82 10.1016/j.apenergy.2020.116329_b0145 10.1016/j.apenergy.2020.116329_b0025 Albert (10.1016/j.apenergy.2020.116329_b0160) 2017 Bódis (10.1016/j.apenergy.2020.116329_b0230) 2019 Lobaccaro (10.1016/j.apenergy.2020.116329_b0075) 2019 León-Vargas (10.1016/j.apenergy.2020.116329_b0045) 2019 Wang (10.1016/j.apenergy.2020.116329_b0005) 2018 Poon (10.1016/j.apenergy.2020.116329_b0120) 2020 Mehdipour Ghazi (10.1016/j.apenergy.2020.116329_b0155) 2017 Stoll (10.1016/j.apenergy.2020.116329_b0215) 2013 Ali (10.1016/j.apenergy.2020.116329_b0225) 2018 Meirich (10.1016/j.apenergy.2020.116329_b0135) 2008 Yang (10.1016/j.apenergy.2020.116329_b0090) 2020; 279 Lukač (10.1016/j.apenergy.2020.116329_b0030) 2013 Krizhevsky (10.1016/j.apenergy.2020.116329_b0095) 2017 10.1016/j.apenergy.2020.116329_b0140 10.1016/j.apenergy.2020.116329_b0175 Yue (10.1016/j.apenergy.2020.116329_b0080) 2011 Ordóñez (10.1016/j.apenergy.2020.116329_b0200) 2010 Karteris (10.1016/j.apenergy.2020.116329_b0115) 2013 Mohajeri (10.1016/j.apenergy.2020.116329_b0060) 2016 |
| References_xml | – year: 2016 ident: b0170 article-title: Plant species classification using deep convolutional neural network publication-title: Biosyst Eng – year: 2016 ident: b0235 article-title: Estimation of Hong Kong’s solar energy potential using GIS and remote sensing technologies publication-title: Renew Energy – year: 2015 ident: b0085 article-title: Estimation of rooftop solar photovoltaic potential of a city publication-title: Sol Energy – reference: Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. In: 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc.; 2015. – year: 2013 ident: b0215 article-title: Potential for rooftop photovoltaics in Tokyo to replace nuclear capacity publication-title: Environ Res Lett – year: 2020 ident: b0150 article-title: SO–CNN based urban functional zone fine division with VHR remote sensing image publication-title: Remote Sens Environ – year: 2018 ident: b0005 article-title: Comparison and quantification analysis method of urban energy consumption features from perspective of urban energy interconnection publication-title: Energy Procedia – year: 2020 ident: b0010 article-title: China’s income gap and inequality under clean energy transformation: a CGE model assessment publication-title: J Clean Prod – year: 2015 ident: b0070 article-title: Solar energy and urban morphology: Scenarios for increasing the renewable energy potential of neighbourhoods in London publication-title: Renew Energy – year: 2010 ident: b0200 article-title: Analysis of the photovoltaic solar energy capacity of residential rooftops in Andalusia (Spain) publication-title: Renew Sustain Energy Rev – year: 2020 ident: b0260 article-title: Recent advancement in smart grid technology: future prospects in the electrical power network publication-title: Ain Shams Eng J – volume: 82 start-page: 929 year: 2008 end-page: 939 ident: b0195 article-title: A method for estimating the geographical distribution of the available roof surface area for large-scale photovoltaic energy-potential evaluations publication-title: Sol Energy – year: 2017 ident: b0050 article-title: On spatial distribution and determinants of urban photovoltaic utilization in China publication-title: Energy Procedia – year: 2016 ident: b0220 article-title: Combining solar resource mapping and energy system integration methods for realistic valuation of urban solar energy potential publication-title: Sol Energy – reference: International Energy Agency IEA. Potential for building integrated photovoltaics. IEA-PVPS Task; 2002. – year: 2017 ident: b0160 article-title: Using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale publication-title: Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. – reference: Google. Overview|Maps Static API | Google Maps Platform 2020. – reference: Wuhan Natural Resources and Planning Bureau. Wuhan Urban Master Plan (2010-2020). Wuhan, Hubei Province, China: Wuhan Natural Resources and Planning Bureau < – year: 2020 ident: b0255 article-title: Assessment of rooftop solar power technical potential in Hanoi city, Vietnam publication-title: J Build Eng – volume: 85 start-page: 1041 year: 2011 end-page: 1055 ident: b0210 article-title: Scalable methodology for the photovoltaic solar energy potential assessment based on available roof surface area: application to Piedmont Region (Italy) publication-title: Sol Energy – year: 2017 ident: b0130 article-title: The importance of facades for the solar PV potential of a Mediterranean city using LiDAR data publication-title: Renew Energy – year: 2020 ident: b0110 article-title: Land-cover classification of multispectral LiDAR data using CNN with optimized hyper-parameters publication-title: ISPRS J Photogramm Remote Sens – year: 2013 ident: b0015 article-title: Development of an environment and energy Geographical Information System (E-GIS) construction model to support environmentally friendly urban planning publication-title: Appl Energy – year: 2008 ident: b0135 article-title: Mapping Guide for a European Urban Atlas publication-title: GSE L Consort – volume: 34 start-page: 345 year: 2010 end-page: 357 ident: b0205 article-title: Quantifying rooftop solar photovoltaic potential for regional renewable energy policy publication-title: Comput Environ Urban Syst – year: 2016 ident: b0060 article-title: Effects of urban compactness on solar energy potential publication-title: Renew Energy – year: 2017 ident: b0095 article-title: ImageNet classification with deep convolutional neural networks publication-title: Commun ACM – volume: 279 start-page: 115786 year: 2020 ident: b0090 article-title: Potential analysis of roof-mounted solar photovoltaics in Sweden publication-title: Appl Energy – reference: ESMAP, SOLARGIS, WB, IFC. Global Solar Atlas. Glob Sol Atlas; 2019. – year: 2018 ident: b0035 article-title: A scalable method for estimating rooftop solar irradiation potential over large regions publication-title: Appl Energy – volume: 171 start-page: 1680 year: 2020 end-page: 1687 ident: b0250 article-title: Deep learning based feature extraction for texture classification publication-title: Procedia Comput Sci – reference: Maruf MH, Haq MA ul, Dey SK, Al Mansur A, Shihavuddin ASM. Adaptation for sustainable implementation of Smart Grid in developing countries like Bangladesh. Energy Reports 2020. – year: 2019 ident: b0075 article-title: A cross-country perspective on solar energy in urban planning: lessons learned from international case studies publication-title: Renew Sustain Energy Rev – year: 2020 ident: b0125 article-title: Solar energy potential of urban buildings in 10 cities of China publication-title: Energy – year: 2018 ident: b0240 article-title: Wuhan Statistical Yearbook – year: 2013 ident: b0030 article-title: Rating of roofs’ surfaces regarding their solar potential and suitability for PV systems, based on LiDAR data publication-title: Appl Energy – year: 2019 ident: b0230 article-title: A high-resolution geospatial assessment of the rooftop solar photovoltaic potential in the European Union publication-title: Renew Sustain Energy Rev – year: 2019 ident: b0180 article-title: Urban solar utilization potential mapping via deep learning technology: a case study of Wuhan publication-title: China. Appl Energy – year: 2019 ident: b0245 article-title: Understanding urban landuse from the above and ground perspectives: a deep learning, multimodal solution publication-title: Remote Sens Environ – reference: Sun Y wei, Hof A, Wang R, Liu J, Lin Y jie, Yang D wei. GIS-based approach for potential analysis of solar PV generation at the regional scale: a case study of Fujian Province. Energy Policy 2013. – reference: Google. Project Sunroof. Google Proj Sunroof; 2016. – reference: European Environment Agency. Copernicus Land Monitoring Service - Urban Atlas; 2017. – year: 2011 ident: b0080 article-title: An evaluation of domestic solar energy potential in Taiwan incorporating land use analysis publication-title: Energy Policy – year: 2018 ident: b0265 article-title: A survey on consumers empowerment, communication technologies, and renewable generation penetration within Smart Grid publication-title: Renew Sustain Energy Rev – year: 2019 ident: b0100 article-title: Joint Deep Learning for land cover and land use classification publication-title: Remote Sens Environ – reference: . – year: 2016 ident: b0105 article-title: Comparing Landsat water index methods for automated water classification in eastern Australia publication-title: Remote Sens Environ – reference: Development P. Solar Urban Planning Berlin 2004. – year: 2020 ident: b0120 article-title: Parametric study of URBAN morphology on building solar energy potential in Singapore context publication-title: Urban Clim – year: 2017 ident: b0155 article-title: Plant identification using deep neural networks via optimization of transfer learning parameters publication-title: Neurocomputing – year: 2019 ident: b0045 article-title: Pre-feasibility of wind and solar systems for residential self-sufficiency in four urban locations of Colombia: implication of new incentives included in Law 1715 publication-title: Renew Energy – year: 2018 ident: b0225 article-title: A preliminary feasibility of roof-mounted solar PV systems in the Maldives publication-title: Renew Sustain Energy Rev – reference: >; 2011. – year: 2019 ident: b0065 article-title: Impact of urban block typology on building solar potential and energy use efficiency in tropical high-density city publication-title: Appl Energy – year: 2018 ident: b0055 article-title: A bottom-up approach for estimating the economic potential of the rooftop solar photovoltaic system considering the spatial and temporal diversity publication-title: Appl Energy – year: 2013 ident: b0115 article-title: Urban solar energy potential in Greece: a statistical calculation model of suitable built roof areas for photovoltaics publication-title: Energy Build – year: 2015 ident: 10.1016/j.apenergy.2020.116329_b0085 article-title: Estimation of rooftop solar photovoltaic potential of a city publication-title: Sol Energy doi: 10.1016/j.solener.2015.03.016 – ident: 10.1016/j.apenergy.2020.116329_b0145 – ident: 10.1016/j.apenergy.2020.116329_b0165 – volume: 82 start-page: 929 year: 2008 ident: 10.1016/j.apenergy.2020.116329_b0195 article-title: A method for estimating the geographical distribution of the available roof surface area for large-scale photovoltaic energy-potential evaluations publication-title: Sol Energy doi: 10.1016/j.solener.2008.03.007 – ident: 10.1016/j.apenergy.2020.116329_b0040 doi: 10.1016/j.enpol.2013.03.002 – ident: 10.1016/j.apenergy.2020.116329_b0175 – year: 2008 ident: 10.1016/j.apenergy.2020.116329_b0135 article-title: Mapping Guide for a European Urban Atlas publication-title: GSE L Consort – volume: 279 start-page: 115786 year: 2020 ident: 10.1016/j.apenergy.2020.116329_b0090 article-title: Potential analysis of roof-mounted solar photovoltaics in Sweden publication-title: Appl Energy doi: 10.1016/j.apenergy.2020.115786 – year: 2016 ident: 10.1016/j.apenergy.2020.116329_b0235 article-title: Estimation of Hong Kong’s solar energy potential using GIS and remote sensing technologies publication-title: Renew Energy doi: 10.1016/j.renene.2016.07.003 – year: 2018 ident: 10.1016/j.apenergy.2020.116329_b0225 article-title: A preliminary feasibility of roof-mounted solar PV systems in the Maldives publication-title: Renew Sustain Energy Rev doi: 10.1016/j.rser.2017.10.019 – year: 2020 ident: 10.1016/j.apenergy.2020.116329_b0260 article-title: Recent advancement in smart grid technology: future prospects in the electrical power network publication-title: Ain Shams Eng J – ident: 10.1016/j.apenergy.2020.116329_b0270 doi: 10.1016/j.egyr.2020.09.010 – volume: 34 start-page: 345 year: 2010 ident: 10.1016/j.apenergy.2020.116329_b0205 article-title: Quantifying rooftop solar photovoltaic potential for regional renewable energy policy publication-title: Comput Environ Urban Syst doi: 10.1016/j.compenvurbsys.2010.01.001 – ident: 10.1016/j.apenergy.2020.116329_b0140 – year: 2019 ident: 10.1016/j.apenergy.2020.116329_b0230 article-title: A high-resolution geospatial assessment of the rooftop solar photovoltaic potential in the European Union publication-title: Renew Sustain Energy Rev doi: 10.1016/j.rser.2019.109309 – year: 2020 ident: 10.1016/j.apenergy.2020.116329_b0010 article-title: China’s income gap and inequality under clean energy transformation: a CGE model assessment publication-title: J Clean Prod – year: 2017 ident: 10.1016/j.apenergy.2020.116329_b0095 article-title: ImageNet classification with deep convolutional neural networks publication-title: Commun ACM doi: 10.1145/3065386 – ident: 10.1016/j.apenergy.2020.116329_b0185 – year: 2019 ident: 10.1016/j.apenergy.2020.116329_b0075 article-title: A cross-country perspective on solar energy in urban planning: lessons learned from international case studies publication-title: Renew Sustain Energy Rev doi: 10.1016/j.rser.2019.03.041 – ident: 10.1016/j.apenergy.2020.116329_b0020 – year: 2018 ident: 10.1016/j.apenergy.2020.116329_b0055 article-title: A bottom-up approach for estimating the economic potential of the rooftop solar photovoltaic system considering the spatial and temporal diversity publication-title: Appl Energy doi: 10.1016/j.apenergy.2018.09.176 – year: 2020 ident: 10.1016/j.apenergy.2020.116329_b0255 article-title: Assessment of rooftop solar power technical potential in Hanoi city, Vietnam publication-title: J Build Eng doi: 10.1016/j.jobe.2020.101528 – volume: 85 start-page: 1041 year: 2011 ident: 10.1016/j.apenergy.2020.116329_b0210 article-title: Scalable methodology for the photovoltaic solar energy potential assessment based on available roof surface area: application to Piedmont Region (Italy) publication-title: Sol Energy doi: 10.1016/j.solener.2011.02.022 – year: 2013 ident: 10.1016/j.apenergy.2020.116329_b0015 article-title: Development of an environment and energy Geographical Information System (E-GIS) construction model to support environmentally friendly urban planning publication-title: Appl Energy – year: 2017 ident: 10.1016/j.apenergy.2020.116329_b0155 article-title: Plant identification using deep neural networks via optimization of transfer learning parameters publication-title: Neurocomputing doi: 10.1016/j.neucom.2017.01.018 – year: 2020 ident: 10.1016/j.apenergy.2020.116329_b0120 article-title: Parametric study of URBAN morphology on building solar energy potential in Singapore context publication-title: Urban Clim doi: 10.1016/j.uclim.2020.100624 – year: 2013 ident: 10.1016/j.apenergy.2020.116329_b0030 article-title: Rating of roofs’ surfaces regarding their solar potential and suitability for PV systems, based on LiDAR data publication-title: Appl Energy doi: 10.1016/j.apenergy.2012.08.042 – year: 2019 ident: 10.1016/j.apenergy.2020.116329_b0065 article-title: Impact of urban block typology on building solar potential and energy use efficiency in tropical high-density city publication-title: Appl Energy – year: 2016 ident: 10.1016/j.apenergy.2020.116329_b0170 article-title: Plant species classification using deep convolutional neural network publication-title: Biosyst Eng doi: 10.1016/j.biosystemseng.2016.08.024 – year: 2020 ident: 10.1016/j.apenergy.2020.116329_b0150 article-title: SO–CNN based urban functional zone fine division with VHR remote sensing image publication-title: Remote Sens Environ – volume: 171 start-page: 1680 year: 2020 ident: 10.1016/j.apenergy.2020.116329_b0250 article-title: Deep learning based feature extraction for texture classification publication-title: Procedia Comput Sci doi: 10.1016/j.procs.2020.04.180 – year: 2015 ident: 10.1016/j.apenergy.2020.116329_b0070 article-title: Solar energy and urban morphology: Scenarios for increasing the renewable energy potential of neighbourhoods in London publication-title: Renew Energy doi: 10.1016/j.renene.2014.06.028 – year: 2013 ident: 10.1016/j.apenergy.2020.116329_b0115 article-title: Urban solar energy potential in Greece: a statistical calculation model of suitable built roof areas for photovoltaics publication-title: Energy Build doi: 10.1016/j.enbuild.2013.03.033 – year: 2016 ident: 10.1016/j.apenergy.2020.116329_b0220 article-title: Combining solar resource mapping and energy system integration methods for realistic valuation of urban solar energy potential publication-title: Sol Energy doi: 10.1016/j.solener.2016.05.061 – year: 2020 ident: 10.1016/j.apenergy.2020.116329_b0110 article-title: Land-cover classification of multispectral LiDAR data using CNN with optimized hyper-parameters publication-title: ISPRS J Photogramm Remote Sens doi: 10.1016/j.isprsjprs.2020.05.022 – year: 2018 ident: 10.1016/j.apenergy.2020.116329_b0240 – year: 2016 ident: 10.1016/j.apenergy.2020.116329_b0105 article-title: Comparing Landsat water index methods for automated water classification in eastern Australia publication-title: Remote Sens Environ doi: 10.1016/j.rse.2015.12.055 – ident: 10.1016/j.apenergy.2020.116329_b0025 – year: 2018 ident: 10.1016/j.apenergy.2020.116329_b0005 article-title: Comparison and quantification analysis method of urban energy consumption features from perspective of urban energy interconnection publication-title: Energy Procedia – year: 2018 ident: 10.1016/j.apenergy.2020.116329_b0035 article-title: A scalable method for estimating rooftop solar irradiation potential over large regions publication-title: Appl Energy doi: 10.1016/j.apenergy.2018.02.008 – year: 2013 ident: 10.1016/j.apenergy.2020.116329_b0215 article-title: Potential for rooftop photovoltaics in Tokyo to replace nuclear capacity publication-title: Environ Res Lett doi: 10.1088/1748-9326/8/1/014042 – year: 2019 ident: 10.1016/j.apenergy.2020.116329_b0100 article-title: Joint Deep Learning for land cover and land use classification publication-title: Remote Sens Environ – year: 2017 ident: 10.1016/j.apenergy.2020.116329_b0160 article-title: Using convolutional networks and satellite imagery to identify patterns in urban environments at a large scale publication-title: Proc. ACM SIGKDD Int. Conf. Knowl. Discov. Data Min. doi: 10.1145/3097983.3098070 – year: 2010 ident: 10.1016/j.apenergy.2020.116329_b0200 article-title: Analysis of the photovoltaic solar energy capacity of residential rooftops in Andalusia (Spain) publication-title: Renew Sustain Energy Rev doi: 10.1016/j.rser.2010.01.001 – year: 2016 ident: 10.1016/j.apenergy.2020.116329_b0060 article-title: Effects of urban compactness on solar energy potential publication-title: Renew Energy doi: 10.1016/j.renene.2016.02.053 – year: 2017 ident: 10.1016/j.apenergy.2020.116329_b0050 article-title: On spatial distribution and determinants of urban photovoltaic utilization in China publication-title: Energy Procedia doi: 10.1016/j.egypro.2017.09.605 – year: 2017 ident: 10.1016/j.apenergy.2020.116329_b0130 article-title: The importance of facades for the solar PV potential of a Mediterranean city using LiDAR data publication-title: Renew Energy doi: 10.1016/j.renene.2017.03.085 – ident: 10.1016/j.apenergy.2020.116329_b0190 – year: 2019 ident: 10.1016/j.apenergy.2020.116329_b0045 article-title: Pre-feasibility of wind and solar systems for residential self-sufficiency in four urban locations of Colombia: implication of new incentives included in Law 1715 publication-title: Renew Energy doi: 10.1016/j.renene.2018.06.087 – year: 2011 ident: 10.1016/j.apenergy.2020.116329_b0080 article-title: An evaluation of domestic solar energy potential in Taiwan incorporating land use analysis publication-title: Energy Policy doi: 10.1016/j.enpol.2011.09.054 – year: 2018 ident: 10.1016/j.apenergy.2020.116329_b0265 article-title: A survey on consumers empowerment, communication technologies, and renewable generation penetration within Smart Grid publication-title: Renew Sustain Energy Rev doi: 10.1016/j.rser.2017.05.208 – year: 2019 ident: 10.1016/j.apenergy.2020.116329_b0245 article-title: Understanding urban landuse from the above and ground perspectives: a deep learning, multimodal solution publication-title: Remote Sens Environ doi: 10.1016/j.rse.2019.04.014 – year: 2019 ident: 10.1016/j.apenergy.2020.116329_b0180 article-title: Urban solar utilization potential mapping via deep learning technology: a case study of Wuhan publication-title: China. Appl Energy – year: 2020 ident: 10.1016/j.apenergy.2020.116329_b0125 article-title: Solar energy potential of urban buildings in 10 cities of China publication-title: Energy doi: 10.1016/j.energy.2020.117038 |
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| Snippet | •New method for evaluating rooftop PV potential in land-use type.•Decline the difficulty of obtaining urban land-use data.•Capability of analyzing several... To strengthen the synergy between urban photovoltaic development and urban planning, which can help to promote photovoltaic and renewable energy development in... |
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| SubjectTerms | case studies China Deep learning education energy land use Neural networks Photovoltaic potential power generation Rooftop solar potential satellites solar energy urban areas Urban land use |
| Title | Deep learning method for evaluating photovoltaic potential of urban land-use: A case study of Wuhan, China |
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