References

1. 1.
   Ahmed A, Nadeem T B, Naqvi A A, Siddiqui M A, Khan M H, Zahid M S B and Ammar S M. 2022. Investigation of PV utilizability on university buildings: a case study of Karachi, Pakistan. Renewable Energy, 195: 238-251
2. 2.
   Al Garni H Z and Awasthi A. 2018. Chapter 2 - Solar PV power plants site selection: a review//Yahyaoui I, ed. Advances in Renewable Energies and Power Technologies. Cambridge: Elsevier: 57-75
3. 3.
   Al-Sanea S A, Zedan M F and Al-Ajlan S A. 2004. Adjustment factors for the ASHRAE clear-sky model based on solar-radiation measurements in Riyadh. Applied Energy, 79(2): 215-237
4. 4.
   Anselmo S and Ferrara M. 2023. Trends and evolution of the GIS-based photovoltaic potential calculation. Energies, 16(23): 7760
5. 5.
   Antonanzas J, Osorio N, Escobar R, Urraca R, Martinez-de-Pison F J and Antonanzas-Torres F. 2016. Review of photovoltaic power forecasting. Solar Energy, 136: 78-111
6. 6.
   Arias-Rosales A and LeDuc P R. 2022. Shadow modeling in urban environments for solar harvesting devices with freely defined positions and orientations. Renewable and Sustainable Energy Reviews, 164: 112522
7. 7.
   Aslani M and Seipel S. 2022. Automatic identification of utilizable rooftop areas in digital surface models for photovoltaics potential assessment. Applied Energy, 306: 118033
8. 8.
   Assouline D, Mohajeri N and Scartezzini J L. 2018a. Estimation of large-scale solar rooftop PV potential for smart grid integration: a methodological review//Amini M H, Boroojeni K G, Iyengar S S, Pardalos P M, Blaabjerg F and Madni A M, eds. Sustainable Interdependent Networks: From Theory to Application. Cham: Springer, 173-219
9. 9.
   Assouline D, Mohajeri N and Scartezzini J L. 2018b. Large-scale rooftop solar photovoltaic technical potential estimation using Random Forests. Applied Energy, 217: 189-211
10. 10.
    Barbón A, Ghodbane M, Bayón L and Said Z. 2022. A general algorithm for the optimization of photovoltaic modules layout on irregular rooftop shapes. Journal of Cleaner Production, 365: 132774
11. 11.
    Benalcazar P, Komorowska A and Kamiński J. 2024. A GIS-based method for assessing the economics of utility-scale photovoltaic systems. Applied Energy, 353: 122044
12. 12.
    Bódis K, Kougias I, Jäger-Waldau A, Taylor N and Szabó S. 2019. A high-resolution geospatial assessment of the rooftop solar photovoltaic potential in the European Union. Renewable and Sustainable Energy Reviews, 114: 109309
13. 13.
    Cavaco A, Canhoto P and Pereira M C. 2021. Procedures for solar radiation data gathering and processing and their application to DNI assessment in southern Portugal. Renewable Energy, 163: 2208-2219
14. 14.
    Cebecauer T and Suri M. 2015. Typical meteorological year data: SolarGIS approach. Energy Procedia, 69: 1958-1969
15. 15.
    Chang J G and Fu M F. 2022. Research on power generation capacity of roof PV power generation system based on PVsyst software. Solar Energy, (11): 81-87
16. 16.
    Chen S, Lu X, Miao Y F, Deng Y, Nielsen C P, Elbot N, Wang Y C, Logan K G, McElroy M B and Hao J M. 2019. The potential of photovoltaics to power the belt and road initiative. Joule, 3(8): 1895-1912
17. 17.
    Chen Z L, Wang F and Feng Y F. 2021. Multi-evaluation of PV utilization potential of urban building roof in Guangzhou. Solar Energy, (11): 64-74
18. 18.
    Cheng C, Gutierrez N P, Blakers A and Stocks M. 2022. GIS-based solar and wind resource assessment and least-cost 100 % renewable electricity modelling for Bolivia. Energy for Sustainable Development, 69: 134-149
19. 19.
    Choi Y, Suh J and Kim S M. 2019. GIS-based solar radiation mapping, site evaluation, and potential assessment: a review. Applied Sciences, 9(9): 1960
20. 20.
    Colak H E, Memisoglu T and Gercek Y. 2020. Optimal site selection for solar photovoltaic (PV) power plants using GIS and AHP: a case study of Malatya Province, Turkey. Renewable Energy, 149: 565-576
21. 21.
    de Almeida Rocha A P, Rodler A, Oliveira R C L F, Virgone J and Mendes N. 2019. A pixel counting technique for sun patch assessment within building enclosures. Solar Energy, 184: 173-186
22. 22.
    de Souza D G B, dos Santos E A, Soma N Y and da Silva C E S. 2021. MCDM-based R&D project selection: a systematic literature review. Sustainability, 13(21): 11626
23. 23.
    Dey D and Subudhi B. 2020. Design, simulation and economic evaluation of 90 kW grid connected Photovoltaic system. Energy Reports, 6: 1778-1787
24. 24.
    Díaz-Cuevas P, Camarillo-Naranjo J M and Pérez-Alcántara J P. 2018. Relational spatial database and multi-criteria decision methods for selecting optimum locations for photovoltaic power plants in the province of Seville (southern Spain). Clean Technologies and Environmental Policy, 20(8): 1889-1902
25. 25.
    Dorman M, Erell E, Vulkan A and Kloog I. 2019. shadow: R package for geometric shadow calculations in an urban environment. The R Journal, 11(1): 287-309
26. 26.
    Esfahani S K, Karrech A, Cameron R, Elchalakani M, Tenorio R and Jerez F. 2021. Optimizing the solar energy capture of residential roof design in the southern hemisphere through Evolutionary Algorithm. Energy and Built Environment, 2(4): 406-424
27. 27.
    Fadlallah S O and Serradj D E B. 2020. Determination of the optimal solar photovoltaic (PV) system for Sudan. Solar Energy, 208: 800-813
28. 28.
    Fard M B, Moradian P, Emarati M, Ebadi M, Chofreh A G and Klemeŝ J J. 2022. Ground-mounted photovoltaic power station site selection and economic analysis based on a hybrid fuzzy best-worst method and geographic information system: a case study Guilan province. Renewable and Sustainable Energy Reviews, 169: 112923
29. 29.
    Fathizad H, Mobin M H, Gholamnia A and Sodaiezadeh H. 2017. Modeling and mapping of solar radiation using geostatistical analysis methods in Iran. Arabian Journal of Geosciences, 10(17): 391
30. 30.
    Feng F and Wang K C. 2021. Merging high-resolution satellite surface radiation data with meteorological sunshine duration observations over China from 1983 to 2017. Remote Sensing, 13(4): 602
31. 31.
    Formolli M, Croce S, Vettorato D, Paparella R, Scognamiglio A, Mainini A G and Lobaccaro G. 2022. Solar energy in urban planning: lesson learned and recommendations from six Italian case studies. Applied Sciences, 12(6): 2950
32. 32.
    Gagnon P, Margolis R, Melius J, Phillips C and Elmore R. 2018. Estimating rooftop solar technical potential across the US using a combination of GIS-based methods, lidar data, and statistical modeling. Environmental Research Letters, 13(2): 024027
33. 33.
    Gassar A A A and Cha S H. 2021. Review of geographic information systems-based rooftop solar photovoltaic potential estimation approaches at urban scales. Applied Energy, 291: 116817
34. 34.
    Gil G M V, Cunha R B A, Di Santo S G, Monaro R M, Costa F F and Filho A J S. 2020. Photovoltaic energy in South America: current state and grid regulation for large-scale and distributed photovoltaic systems. Renewable Energy, 162: 1307-1320
35. 35.
    Gracia Amillo A M, Taylor N, Martinez A M, Dunlop E D, Mavrogiorgios P, Fahl F, Arcaro G and Pinedo I. 2021. Adapting PVGIS to trends in climate, technology and user needs//Proceedings of the 38th European Photovoltaic Solar Energy Conference and Exhibition (PVSEC). [s.l.]: [s.n.], 907-911
36. 36.
    Guermoui M, Melgani F, Gairaa K and Mekhalfi M L. 2020. A comprehensive review of hybrid models for solar radiation forecasting. Journal of Cleaner Production, 258: 120357
37. 37.
    Hafeznia H, Yousefi H and Astaraei F R. 2017. A novel framework for the potential assessment of utility-scale photovoltaic solar energy, application to eastern Iran. Energy Conversion and Management, 151: 240-258
38. 38.
    Han J Y, Chen Y C and Li S Y. 2022. Utilising high-fidelity 3D building model for analysing the rooftop solar photovoltaic potential in urban areas. Solar Energy, 235: 187-199
39. 39.
    Hofierka J and Zlocha M. 2012. A new 3-D solar radiation model for 3-D city models. Transactions in GIS, 16(5): 681-690
40. 40.
    Hong T, Lee M, Koo C, Jeong K and Kim J. 2017. Development of a method for estimating the rooftop solar photovoltaic (PV) potential by analyzing the available rooftop area using Hillshade analysis. Applied Energy, 194: 320-332
41. 41.
    Hooper T, Armstrong A and Vlaswinkel B. 2021. Environmental impacts and benefits of marine floating solar. Solar Energy, 219: 11-14
42. 42.
    Hu X Y, Wang Y Z, Zhou T and Mwakapesa D S. 2024. Building facade structure extraction method based on three-dimensional laser point cloud by considering semantic information. Annals of GIS, 30(3): 291-305
43. 43.
    Huang S L, Rich P M, Crabtree R L, Potter C S and Fu P. 2008. Modeling monthly near-surface air temperature from solar radiation and lapse rate: application over complex terrain in Yellowstone national park. Physical Geography, 29(2): 158-178
44. 44.
    Jacobson M Z and Jadhav V. 2018. World estimates of PV optimal tilt angles and ratios of sunlight incident upon tilted and tracked PV panels relative to horizontal panels. Solar Energy, 169: 55-66
45. 45.
    Jiang M K, Li J S, Wei W D, Miao J W, Zhang P F, Qian H Q, Liu J M and Yan J Y. 2020. Using existing infrastructure to realize low-cost and flexible photovoltaic power generation in areas with high-power demand in China. iScience, 23(12): 101867
46. 46.
    Joshi S, Mittal S, Holloway P, Shukla P R, Gallachóir B Ó and Glynn J. 2021. High resolution global spatiotemporal assessment of rooftop solar photovoltaics potential for renewable electricity generation. Nature Communications, 12(1): 5738
47. 47.
    Julieta S R, José-Julio R B and Pablo Y R. 2022. A methodology to estimate the photovoltaic potential on parking spaces and water deposits. The case of the Canary Islands. Renewable Energy, 189: 1046-1062
48. 48.
    Jung J, Han S U and Kim B. 2019. Digital numerical map-oriented estimation of solar energy potential for site selection of photovoltaic solar panels on national highway slopes. Applied Energy, 242: 57-68
49. 49.
    Jurasz J K, Dąbek P B and Campana P E. 2020. Can a city reach energy self-sufficiency by means of rooftop photovoltaics? Case study from Poland. Journal of Cleaner Production, 245: 118813
50. 50.
    Ko L, Wang J C, Chen C Y and Tsai H Y. 2015. Evaluation of the development potential of rooftop solar photovoltaic in Taiwan. Renewable Energy, 76: 582-595
51. 51.
    Kumar D S, Yagli G M, Kashyap M and Srinivasan D. 2020. Solar irradiance resource and forecasting: a comprehensive review. IET Renewable Power Generation, 14(10): 1641-1656
52. 52.
    Kumar N M, Chakraborty S, Yadav S K, Singh J and Chopra S S. 2022. Advancing simulation tools specific to floating solar photovoltaic systems – Comparative analysis of field-measured and simulated energy performance. Sustainable Energy Technologies and Assessments, 52: 102168
53. 53.
    Kumar R, Rajoria C S, Sharma A and Suhag S. 2021. Design and simulation of standalone solar PV system using PVsyst Software: a case study. Materialstoday: Proceedings, 46(11): 5322-5328
54. 54.
    Kutlu E C, Durusoy B, Ozden T and Akinoglu B G. 2022. Technical potential of rooftop solar photovoltaic for Ankara. Renewable Energy, 185: 779-789
55. 55.
    Lefèvre M, Oumbe A, Blanc P, Espinar B, Gschwind B, Qu Z, Wald L, Schroedter-Homscheidt M, Hoyer-Klick C, Arola A, Benedetti A, Kaiser J W and Morcrette J J. 2013. McClear: a new model estimating downwelling solar radiation at ground level in clear-sky conditions. Atmospheric Measurement Techniques, 6(9): 2403-2418
56. 56.
    Letu H, Nakajima T Y, Wang T X, Shang H Z, Ma R, Yang K, Baran A J, Riedi J, Ishimoto H, Yoshida M, Shi C, Khatri P, Du Y H, Chen L F and Shi J C. 2022. A new benchmark for surface radiation products over the East Asia–Pacific region retrieved from the Himawari-8/AHI next-generation geostationary satellite. Bulletin of the American Meteorological Society, 103(3): E873-E888
57. 57.
    Li S Y and Han J Y. 2022. The impact of shadow covering on the rooftop solar photovoltaic system for evaluating self-sufficiency rate in the concept of nearly zero energy building. Sustainable Cities and Society, 80: 103821
58. 58.
    Liao X, Zhu R and Wong M S. 2022. Simplified estimation modeling of land surface solar irradiation: a comparative study in Australia and China. Sustainable Energy Technologies and Assessments, 52: 102323
59. 59.
    Liao X, Zhu R, Wong M S, Heo J, Chan P W and Kwok C Y T. 2023. Fast and accurate estimation of solar irradiation on building rooftops in Hong Kong: a machine learning-based parameterization approach. Renewable Energy, 216: 119034
60. 60.
    Limouni T, Yaagoubi R, Bouziane K, Guissi K and Baali E H. 2023. Accurate one step and multistep forecasting of very short-term PV power using LSTM-TCN model. Renewable Energy, 205: 1010-1024
61. 61.
    Liu M Y, Peng J Q, Xiao R, Chen X, Song M Y, Deng Z and Chen Y X. 2023. Study on deep learning-based roof photovoltaic area identification and photovoltaic power generation potential. Building Science, 39(6): 151-158
62. 62.
    Liu Z R, Liu X, Zhang H R and Yan D. 2023. Integrated physical approach to assessing urban-scale building photovoltaic potential at high spatiotemporal resolution. Journal of Cleaner Production, 388: 135979
63. 63.
    Lu Y B, Wang L C, Zhu C M, Zou L, Zhang M, Feng L and Cao Q. 2023. Predicting surface solar radiation using a hybrid radiative Transfer–Machine learning model. Renewable and Sustainable Energy Reviews, 173: 113105
64. 64.
    Mangiante M J, Whung P Y, Zhou L X, Porter R, Cepada A, Campirano Jr E, Licon Jr D, Lawrence R and Torres M. 2020. Economic and technical assessment of rooftop solar photovoltaic potential in Brownsville, Texas, U.S.A. Computers, Environment and Urban Systems 80: 101450
65. 65.
    Minaei F, Minaei M, Kougias I, Shafizadeh-Moghadam H and Hosseini S A. 2021. Rural electrification in protected areas: a spatial assessment of solar photovoltaic suitability using the fuzzy best worst method. Renewable Energy, 176: 334-345
66. 66.
    Mohammadi S A D and Gezegin C. 2022. Design and simulation of grid-connected solar PV system using PVSYST, PVGIS and HOMER software. International Journal of Pioneering Technology and Engineering, 1(1): 36-41
67. 67.
    Muhammed E, El-Shazly A and Morsy S. 2023. Building rooftop extraction using machine learning algorithms for solar photovoltaic potential estimation. Sustainability, 15(14): 11004
68. 68.
    Nadeem T B, Siddiqui M, Khalid M and Asif M. 2023. Distributed energy systems: a review of classification, technologies, applications, and policies. Energy Strategy Reviews, 48: 101096
69. 69.
    Narjabadifam N, Al-Saffar M, Zhang Y Q, Nofech J, Cen A C, Awad H, Versteege M and Gül M. 2022. Framework for mapping and optimizing the solar rooftop potential of buildings in urban systems. Energies, 15(5): 1738
70. 70.
    Obeng M, Gyamfi S, Derkyi N S, Kabo-bah A T and Peprah F. 2020. Technical and economic feasibility of a 50 MW grid-connected solar PV at UENR Nsoatre Campus. Journal of Cleaner Production, 247: 119159
71. 71.
    Obiwulu A U, Erusiafe N, Olopade M A and Nwokolo S C. 2022. Modeling and estimation of the optimal tilt angle, maximum incident solar radiation, and global radiation index of the photovoltaic system. Heliyon, 8(6): e09598
72. 72.
    Omar O A, El Fadil H, El Fezazi N E, Oumimoun Z, Errouhi A A and Choukai O. 2024. Real yields and PVSYST simulations: comparative analysis based on four photovoltaic installations at Ibn Tofail University. Energy Harvesting and Systems, 11(1): 20230064
73. 73.
    Ouchani F Z, Jbaihi O, Merrouni A A, Maaroufi M and Ghennioui A. 2021. Yield analysis and economic assessment for GIS-mapping of large scale solar PV potential and integration in Morocco. Sustainable Energy Technologies and Assessments, 47: 101540
74. 74.
    Pan D, Bai Y J, Chang M, Wang X M and Wang W W. 2022. The technical and economic potential of urban rooftop photovoltaic systems for power generation in Guangzhou, China. Energy and Buildings, 277: 112591
75. 75.
    Prăvălie R, Patriche C and Bandoc G. 2019. Spatial assessment of solar energy potential at global scale. A geographical approach. Journal of Cleaner Production, 209: 692-721
76. 76.
    Qian Z, Chen M, Yang Y, Zhong T, Zhang F, Zhu R, Zhang K, Zhang Z X, Sun Z, Ma P L, Lü G N, Ye Y and Yan J Y. 2022a. Vectorized dataset of roadside noise barriers in China using street view imagery. Earth System Science Data, 14(9): 4057-4076
77. 77.
    Qian Z, Chen M, Zhong T, Zhang F, Zhu R, Zhang Z X, Zhang K, Sun Z and Lü G N. 2022b. Deep Roof Refiner: a detail-oriented deep learning network for refined delineation of roof structure lines using satellite imagery. International Journal of Applied Earth Observation and Geoinformation, 107: 102680
78. 78.
    Qiu T Z, Wang L C, Lu Y B, Zhang M, Qin W M, Wang S Q and Wang L Z. 2022. Potential assessment of photovoltaic power generation in China. Renewable and Sustainable Energy Reviews, 154: 111900
79. 79.
    Raptis P I, Kazadzis S, Psiloglou B, Kouremeti N, Kosmopoulos P and Kazantzidis A. 2017. Measurements and model simulations of solar radiation at tilted planes, towards the maximization of energy capture. Energy, 130: 570-580
80. 80.
    Rediske G, Siluk J C M, Michels L, Rigo P D, Rosa C B and Cugler G. 2020. Multi-criteria decision-making model for assessment of large photovoltaic farms in Brazil. Energy, 197: 117167
81. 81.
    Ren H S, Sun Y J, Tse C F N and Fan C. 2023. Optimal packing and planning for large-scale distributed rooftop photovoltaic systems under complex shading effects and rooftop availabilities. Energy, 274: 127280
82. 82.
    Robledo J, Leloux J, Lorenzo E and Gueymard C A. 2019. From video games to solar energy: 3D shading simulation for PV using GPU. Solar Energy, 193: 962-980
83. 83.
    Rodríguez L R, Duminil E, Ramos J S and Eicker U. 2017. Assessment of the photovoltaic potential at urban level based on 3D city models: a case study and new methodological approach. Solar Energy, 146: 264-275
84. 84.
    Rozmi M D A B, Thirunavukkarasu G S, Jamei E, Seyedmahmoudian M, Mekhilef S, Stojcevski A and Horan B. 2019. Role of immersive visualization tools in renewable energy system development. Renewable and Sustainable Energy Reviews, 115: 109363
85. 85.
    Scott J A, Ho W and Dey P K. 2012. A review of multi-criteria decision-making methods for bioenergy systems. Energy, 42(1): 146-156
86. 86.
    Sidek M H M, Azis N, Hasan W Z W, Ab Kadir M Z A, Shafie S and Radzi M A M. 2017. Automated positioning dual-axis solar tracking system with precision elevation and azimuth angle control. Energy, 124: 160-170
87. 87.
    Singh R. 2020. Approximate rooftop solar PV potential of Indian cities for high-level renewable power scenario planning. Sustainable Energy Technologies and Assessments, 42: 100850
88. 88.
    Sreenath S, Sudhakar K and AF Y. 2021. 7E analysis of a conceptual utility-scale land-based solar photovoltaic power plant. Energy, 219: 119610
89. 89.
    Sun T, Shan M, Rong X and Yang X D. 2022. Estimating the spatial distribution of solar photovoltaic power generation potential on different types of rural rooftops using a deep learning network applied to satellite images. Applied Energy, 315: 119025
90. 90.
    Tang W J, He J M, Qi J W and Yang K. 2023. A dense station-based, long-term and high-accuracy dataset of daily surface solar radiation in China. Earth System Science Data, 15(10): 4537-4551
91. 91.
    Tang W J, Yang K, Qin J, Li X and Niu X L. 2019. A 16-year dataset (2000‐2015) of high-resolution (3 h, 10 km) global surface solar radiation. Earth System Science Data, 11(4): 1905-1915
92. 92.
    Tian S, Yang G Q, Du S H, Zhuang D, Zhu K, Zhou X, Jin X, Ye Y, Li P X and Shi X. 2024. An innovative method for evaluating the urban roof photovoltaic potential based on open-source satellite images. Renewable Energy, 224: 120075
93. 93.
    Vo A V and Laefer D F. 2019. A Big Data approach for comprehensive urban shadow analysis from airborne laser scanning point clouds. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 4: 131-137
94. 94.
    Wadhawan S R and Pearce J M. 2017. Power and energy potential of mass-scale photovoltaic noise barrier deployment: a case study for the U.S. Renewable and Sustainable Energy Reviews, 80: 125-132
95. 95.
    Wang J J, Jing Y Y, Zhang C F and Zhao J H. 2009. Review on multi-criteria decision analysis aid in sustainable energy decision-making. Renewable and Sustainable Energy Reviews, 13(9): 2263-2278
96. 96.
    Wang L C, Xu S Z, Gong Y K, Ning J, Zhang X D and Zhao Y. 2022a. High resolution photovoltaic power generation potential assessments of rooftop in China. Energy Reports, 8: 14545-14553
97. 97.
    Wang P, Yu P, Huang L and Zhang Y H. 2022b. An integrated technical, economic, and environmental framework for evaluating the rooftop photovoltaic potential of old residential buildings. Journal of Environmental Management, 317: 115296
98. 98.
    Wang P, Zhang S N, Pu Y R, Cao S C and Zhang Y H. 2021. Estimation of photovoltaic power generation potential in 2020 and 2030 using land resource changes: an empirical study from China. Energy, 219: 119611
99. 99.
    Wang S Q, Li Z W, Wang H, Chen J L and Li Z H. 2021. Analysis on PV power generation utilization potential of urban building rooftop under new supply mode. Solar Energy, (6): 11-17
100. 100.
     Wang X Y, Zhang X K, Zhu S J, Ren J W, Causone F, Ye Y, Jin X, Zhou X and Shi X. 2023. A novel and efficient method for calculating beam shadows on exterior surfaces of buildings in dense urban contexts. Building and Environment, 229: 109937
101. 101.
     Wu Y N, Geng S, Zhang H B and Gao M. 2014. Decision framework of solar thermal power plant site selection based on linguistic Choquet operator. Applied Energy, 136: 303-311
102. 102.
     Xie Y, Sengupta M, Yang J, Buster G, Benton B, Habte A and Liu Y G. 2023. Integration of a physics-based direct normal irradiance (DNI) model to enhance the National Solar Radiation Database (NSRDB). Solar Energy, 266: 112195
103. 103.
     Xu L T, Ding P, Zhang Y, Huang Y J, Li J M and Ma R H. 2024. Sensitivity analysis of the shading effects from obstructions at different positions on solar photovoltaic panels. Energy, 290: 130229
104. 104.
     Xu S, Li Z X, Zhang C, Huang Z J, Tian J, Luo Y Q and Du H. 2021. A method of calculating urban-scale solar potential by evaluating and quantifying the relationship between urban block typology and occlusion coefficient: a case study of Wuhan in Central China. Sustainable Cities and Society, 64: 102451
105. 105.
     Yalcin A S, Kilic H S and Delen D. 2022. The use of multi-criteria decision-making methods in business analytics: a comprehensive literature review. Technological Forecasting and Social Change, 174: 121193
106. 106.
     Yang Q, Huang T Y, Wang S G, Li J S, Dai S Q, Wright S, Wang Y X and Peng H W. 2019. A GIS-based high spatial resolution assessment of large-scale PV generation potential in China. Applied Energy, 247: 254-269
107. 107.
     Yang Y, Campana P E, Stridh B and Yan J Y. 2020. Potential analysis of roof-mounted solar photovoltaics in Sweden. Applied Energy, 279: 115786
108. 108.
     Yildirim D, Büyüksalih G and Şahin A D. 2021. Rooftop photovoltaic potential in Istanbul: calculations based on LiDAR data, measurements and verifications. Applied Energy, 304: 117743
109. 109.
     Yu S W, Han R L and Zhang J J. 2023. Reassessment of the potential for centralized and distributed photovoltaic power generation in China: on a prefecture-level city scale. Energy, 262: 125436
110. 110.
     Zeng Z L, Wang Z M, Gui K, Yan X Y, Gao M, Luo M, Geng H, Liao T T, Li X, An J C, Liu H Z, He C, Ning G C and Yang Y J. 2020. Daily global solar radiation in China estimated from high-density meteorological observations: a random forest model framework. Earth and Space Science, 7(2): e2019EA001058
111. 111.
     Zhang K, Chen M, Yang Y, Zhong T, Zhu R, Zhang F, Qian Z, Lü G N and Yan J Y. 2022a. Quantifying the photovoltaic potential of highways in China. Applied Energy, 324: 119600
112. 112.
     Zhang K, Chen M, Zhu R, Zhang F, Zhong T, Lin J, You L L, Lü G N and Yan J Y. 2024a. Integrating photovoltaic noise barriers and electric vehicle charging stations for sustainable city transportation. Sustainable Cities and Society, 100: 104996
113. 113.
     Zhang K, Qian Z, Yang Y, Chen M, Zhong T, Zhu R, Lv G N and Yan J Y. 2022b. Using street view images to identify road noise barriers with ensemble classification model and geospatial analysis. Sustainable Cities and Society, 78: 103598
114. 114.
     Zhang K, Wang D J, Chen M, Zhu R, Zhang F, Zhong T, Qian Z, Wang Y Z, Li H Y, Wang Y J, Lü G N and Yan J Y. 2024b. Power generation assessment of photovoltaic noise barriers across 52 major Chinese cities. Applied Energy, 361: 122839
115. 115.
     Zhang Q, Xin X Z, Zhang H L, Li Y, Li X J and Yi C X. 2018. Suitability analysis of photovoltaic power plants in China using remote sensing data and multi-criteria evaluation. Journal of Geo-information Science, 20(1): 119-127
116. 116.
     Zhang T P, Stackhouse Jr P W, Chandler W S and Westberg D J. 2014. Application of a global-to-beam irradiance model to the NASA GEWEX SRB dataset: an extension of the NASA Surface meteorology and Solar Energy datasets. Solar Energy, 110: 117-131
117. 117.
     Zhang T P, Stackhouse Jr P W, Macpherson B and Mikovitz J C. 2024c. A CERES-based dataset of hourly DNI, DHI and global tilted irradiance (GTI) on equatorward tilted surfaces: derivation and comparison with the ground-based BSRN data. Solar Energy, 274: 112538
118. 118.
     Zhang X D, Zhou J, Liang S L and Wang D D. 2021. A practical reanalysis data and thermal infrared remote sensing data merging (RTM) method for reconstruction of a 1-km all-weather land surface temperature. Remote Sensing of Environment, 260: 112437
119. 119.
     Zhang Y C and Rossow W B. 2023. Global radiative flux profile data set: revised and extended. Journal of Geophysical Research: Atmospheres, 128(5): e2022JD037340
120. 120.
     Zhang Y H, Ren J, Pu Y R and Wang P. 2020. Solar energy potential assessment: a framework to integrate geographic, technological, and economic indices for a potential analysis. Renewable Energy, 149: 577-586
121. 121.
     Zhang Z X, Chen M, Zhong T, Zhu R, Qian Z, Zhang F, Yang Y, Zhang K, Santi P, Wang K C, Pu Y X, Tian L X, Lü G N and Yan J Y. 2023. Carbon mitigation potential afforded by rooftop photovoltaic in China. Nature Communications, 14(1): 2347
122. 122.
     Zhang Z X, Qian Z, Zhong T, Chen M, Zhang K, Yang Y, Zhu R, Zhang F, Zhang H R, Zhou F Z, Yu J N, Zhang B Y, Lü G N and Yan J Y. 2022c. Vectorized rooftop area data for 90 cities in China. Scientific Data, 9(1): 66
123. 123.
     Zhao Z Y, Zhang S Y and Ge X. 2023. Construction of regional large-scale photovoltaic power station site selection indicator system based on geographic information technology and fuzzy analytic hierarchy process-fuzzy decision making trial and evaluation laboratory methods (AHP-DEMATEL): a case study of Inner Mongolia. Science and Technology Management Research, 43(6): 78-87
124. 124.
     Zhong Q, Nelson J R, Tong D Q and Grubesic T H. 2022. A spatial optimization approach to increase the accuracy of rooftop solar energy assessments. Applied Energy, 316: 119128
125. 125.
     Zhong T, Zhang K, Chen M, Wang Y J, Zhu R, Zhang Z X, Zhou Z X, Qian Z, Lv G N and Yan J Y. 2021. Assessment of solar photovoltaic potentials on urban noise barriers using street-view imagery. Renewable Energy, 168: 181-194
126. 126.
     Zhong X H, Ruiz-Arias J A and Kleissl J. 2016. Dissecting surface clear sky irradiance bias in numerical weather prediction: application and corrections to the New Goddard Shortwave Scheme. Solar Energy, 132: 103-113
127. 127.
     Zhu R, Cheng C, Santi P, Chen M, Zhang X H, Mazzarello M, Wong M S and Ratti C. 2022. Optimization of photovoltaic provision in a three-dimensional city using real-time electricity demand. Applied Energy, 316: 119042
128. 128.
     Zhu R, Wong M S, You L L, Santi P, Nichol J, Ho H C, Lu L and Ratti C. 2020. The effect of urban morphology on the solar capacity of three-dimensional cities. Renewable Energy, 153: 1111-1126
129. 129.
     Zoma F and Sawadogo M. 2023. A multicriteria approach for biomass availability assessment and selection for energy production in Burkina Faso: a hybrid AHP-TOPSIS approach. Heliyon, 9(10): e20999