# Estimating the Photovoltaic Potential of Building Facades and Roofs Using the Industry Foundation Classes

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

^{2}), that of the facade is 8240 m

^{2}. The photovoltaic potential of the simulated building could reach 1054.69 MWh/year. The sensitivity studies of the grid resolution, the time interval and the computation time confirmed the reasonability of the determined conditions. The method proposed offers great potential for energy planning departments and the improved utilization of buildings.

## 1. Introduction

## 2. Methods

#### 2.1. Analysis of IFC Data

#### 2.2. Determination of Building External Surface and Photovoltaic Installation Area

_{i}represents the two-dimensional contour of the floor (i.e., the top-view projection of the facade), F

_{i}represents the height of the floor from the ground and H

_{i}represents the height of the current floor from the top surface of the floor slab to the ceiling (including the ceiling thickness). As shown in Figure 6, we traverse the contour set $B=\left\{{C}_{i}\right|i\in n,n\ne 0\}$ of the building floors and merge all floors with the same two-dimensional contour shape from the sth floor to the tth floor. The following set is obtained:${S}_{k}=\{({C}_{i},{G}_{k},{F}_{k},{H}_{k})|{G}_{i}={G}_{k},i=\left\{s,s+1,s+2,\dots t\right\}\}$, where ${F}_{k}=Min\left({F}_{i}\right)$ represents the lowest ground height in the set and ${H}_{k}=Sum\left({H}_{i}\right)$ represents the accumulation of floor heights in the set.

#### 2.3. Photovoltaic Energy Simulation Calculation

_{t}= I

_{b}+ I

_{d}+ I

_{r}, where I

_{t}is the total solar radiation and I

_{b}, I

_{d}and I

_{r}represent direct, diffuse and ground-reflected radiation, respectively. The isotropic model was used for the solar radiation evaluation [48,51]. Due to the complex reflections between the building and the impact of the reflective radiation on urban areas being negligible, as in [52,53,54], only the ground-reflected radiation was considered.

_{b}was calculated as ${I}_{b}={I}_{0}{\tau}_{a}{\tau}_{b}\mathrm{cos}i$, where I

_{0}is the solar flux outside the atmosphere, in W/m

^{2}[48,55,56]; ${\tau}_{a}$ represents the atmospheric clearness index [57,58]; ${\tau}_{b}$ is the atmospheric transmittance for beam radiation [56]; and i is the angle between the normal to the surface and the direction to the sun, in degrees [48].

_{0}is the solar constant, which was 1367 W/m

^{2}in this model [55], and N is the day (N was 1 on January 1 and 365 on December 31).

_{S}represents the hourly angle (in degrees). When the sun is on the meridian, H

_{S}= 0°, and it decreases at a rate of 15°/h, with positive values in the morning and negative values in the afternoon [47,48].

_{d}was calculated as follows:

_{r}was calculated as

_{1}and t

_{2}. At t

_{1}, a ray from the surface of the building points to the position of the sun at time t

_{1}. The analysis determined whether the ray intersected with buildings or the environment’s triangular patches. As time t

_{1}was intersected, it was sheltered and the sun could not reach the selected point on the building surface. For the same reason, time t

_{2}was evaluated.

_{PV}represents photovoltaic energy generation, in MWh; η is the average efficiency of the photovoltaic system, which was set to 0.2 [62,63]; S

_{a}is the available area for the photovoltaic panel; and H

_{t}is the total solar radiation in the available area over a period of time.

## 3. Simulation Results

#### 3.1. Extraction of Available Installation Areas of the Building

^{2}and a height of 91 m. The facade orientations were 10.635° east–north, 10.635° north–west, 10.635° west–south and 10.635° south–east. There were 14 residential buildings around it; the three-dimensional models of these surrounding buildings played a key role when performing shadow occlusion calculations on simulated buildings.

^{2}; the higher the resolution, the larger the time required for the calculation. Hofierka and Zlocha [46] set the voxel to 2.5 m, which reduced the calculation time but also decreased the accuracy. Some scholars have defined the spatial resolution of rooftops and facades as 1 m

^{2}[10,42,52]. Based on previous studies and considering photovoltaic panels of approximately 1 m

^{2}, the grid center points were set at 1 m intervals. All grid center points at less than 1 m interval were deleted. As such, the number of grid center points represented the number of photovoltaic panels. The statistics describing the area of each building element are shown in Table 1.

^{2}, but the actual available area of the entire building for photovoltaic panel installation was only 8995 m

^{2}. The roof utilization rate was the highest, reaching 96.36%. The proportion of installable area on the facades (excluding windows) was relatively low, only 38.27%. Owing to the screening of areas for photovoltaic panels, the available installation area was smaller than the area of each building element. This was particularly reflected in the facade. Constructing the grid center points of the facade relied on contour lines and the construction of grid center points was not carried out for line segments of <1 m. Here, the outline of the building had a large number of line segments of <1 m. The windows in the facade were treated separately. Therefore, the installation of photovoltaic panels on windows in the facade and the installation of photovoltaic panels on the facades (excluding the windows) should meet the requirement of excluding areas with length <1 m. Therefore, the suitable area represented by the number of grid center points constructed was smaller than the total area of building elements. However, although the proportion of the suitable area of the facade was not high, the area was much larger than that of the roof and, considering these constraints, could better meet the building’s energy demand.

#### 3.2. Estimation of Photovoltaic Energy Generation Potential

## 4. Discussion

#### 4.1. Comparison with Other Methods

#### 4.2. Relationship between Calculation Time and Accuracy

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## References

- Cheng, L.; Xu, H.; Li, S.; Chen, Y.; Zhang, F.; Li, M. Use of LiDAR for calculating solar irradiance on roofs and facades of buildings at city scale: Methodology, validation, and analysis. ISPRS-J. Photogramm. Remote Sens.
**2018**, 138, 12–29. [Google Scholar] [CrossRef] - Othman, A.R.; Rushdi, A.T. Potential of building integrated photovoltaic application on roof top of residential development in Shah Alam. Procedia-Soc. Behav. Sci.
**2014**, 153, 491–500. [Google Scholar] [CrossRef] [Green Version] - Karteris, M.; Theodoridou, I.; Mallinis, G.; Papadopoulos, A.M. Facade photovoltaic systems on multifamily buildings: An urban scale evaluation analysis using geographical information systems. Renew. Sustain. Energy Rev.
**2014**, 39, 912–933. [Google Scholar] [CrossRef] - Theodoridou, I.; Karteris, M.; Mallinis, G.; Papadopoulos, A.M.; Hegger, M. Assessment of retrofitting measures and solar systems’ potential in urban areas using Geographical Information Systems: Application to a Mediterranean city. Renew. Sustain. Energy Rev.
**2012**, 16, 6239–6261. [Google Scholar] [CrossRef] - Liu, G.X.; Wu, W.X.; Ge, Q.S.; Dai, E.; Wan, Z.; Zhou, Y. A GIS method for assessing roof-mounted solar energy potential: A case study in Jiangsu, China. Environ. Eng. Manag. J.
**2011**, 10, 843–848. [Google Scholar] - Kabir, M.H.; Endlicher, W.; Jägermeyr, J. Calculation of bright roof-tops for solar PV applications in Dhaka Megacity, Bangladesh. Renew. Energy
**2010**, 35, 1760–1764. [Google Scholar] [CrossRef] - Fath, K.; Stengel, J.; Sprenger, W.; Wilson, H.R.; Schultmann, F.; Kuhn, T.E. A method for predicting the economic potential of (building-integrated) photovoltaics in urban areas based on hourly Radiance simulations. Sol. Energy
**2015**, 116, 357–370. [Google Scholar] [CrossRef] - Strzalka, A.; Alam, N.; Duminil, E.; Coors, V.; Eicker, U. Large scale integration of photovoltaics in cities. Appl. Energy
**2012**, 93, 413–421. [Google Scholar] [CrossRef] - Hofierka, J.; Kaňuk, J. Assessment of photovoltaic potential in urban areas using open-source solar radiation tools. Renew. Energy
**2009**, 34, 2206–2214. [Google Scholar] [CrossRef] - Redweik, P.; Catita, C.; Brito, M. Solar energy potential on roofs and facades in an urban landscape. Sol. Energy
**2013**, 97, 332–341. [Google Scholar] [CrossRef] - Szabó, S.; Enyedi, P.; Horváth, M.; Kovács, Z.; Burai, P.; Csoknyai, T.; Szabó, G. Automated registration of potential locations for solar energy production with Light Detection and Ranging (LiDAR) and small format photogrammetry. J. Clean. Prod.
**2016**, 112, 3820–3829. [Google Scholar] [CrossRef] - Jakubiec, J.A.; Reinhart, C.F. A method for predicting city-wide electricity gains from photovoltaic panels based on LiDAR and GIS data combined with hourly Daysim simulations. Sol. Energy
**2013**, 93, 127–143. [Google Scholar] [CrossRef] - Rodríguez, L.R.; Duminil, E.; Ramos, J.S.; Eicker, U. Assessment of the photovoltaic potential at urban level based on 3D city models: A case study and new methodological approach. Sol. Energy
**2017**, 146, 264–275. [Google Scholar] [CrossRef] - Garnett, R.; Freeburn, J.T. Visual acceptance of library-generated citygml lod3 building models. Cartograph. Int. J. Geogr. Inf. Geovis.
**2014**, 49, 218–224. [Google Scholar] [CrossRef] - Haala, N.; Kada, M. Panoramic scenes for texture mapping of 3D city models. In Proceedings of the ISPRS Working Group V/5: Panoramic Photogrammetry Workshop, Berlin, Germany, 24–25 February 2005; Volume 36. Part 5/W8. [Google Scholar]
- Grammatikopoulos, L.; Kalisperakis, I.; Petsa, E. Automatic Image Orientation for Accurate Texture Mapping of 3D City Models. In Proceedings of the International Conference ‘Science in Technology’ SCinTE, Athens, Greece, 5–7 November 2015. [Google Scholar]
- Biljecki, F.; Stoter, J.; Ledoux, H.; Zlatanova, S.; Çöltekin, A. Applications of 3D city models: State of the art review. ISPRS Int. Geo-Inf.
**2015**, 4, 2842–2889. [Google Scholar] [CrossRef] [Green Version] - Wong, M.S.; Zhu, R.; Liu, Z.; Lu, L.; Peng, J.; Tang, Z.; Lo, C.H.; Chan, W.K. Estimation of Hong Kong’s solar energy potential using GIS and remote sensing technologies. Renew. Energy
**2016**, 99, 325–335. [Google Scholar] [CrossRef] - Zhang, W.; Wong, N.H.; Zhang, Y.; Chen, Y.; Tong, S.; Zheng, Z.; Chen, J. Evaluation of the photovoltaic potential in built environment using spatial data captured by unmanned aerial vehicles. Energy Sci. Eng.
**2019**, 7, 2011–2025. [Google Scholar] [CrossRef] [Green Version] - Azhar, S. Building information modeling (BIM): Trends, benefits, risks, and challenges for the AEC industry. Leadersh. Manag. Eng.
**2011**, 11, 241–252. [Google Scholar] [CrossRef] - Wang, H.; Pan, Y.; Luo, X. Integration of BIM and GIS in sustainable built environment: A review and bibliometric analysis. Autom. Constr.
**2019**, 103, 41–52. [Google Scholar] [CrossRef] - Pang, Y.Y.; Zhang, C.; Zhou, L.C.; Lin, B.X.; Lv, G.N. Extracting Indoor Space Information in Complex Building Environments. ISPRS Int. Geo-Inf.
**2018**, 7, 321. [Google Scholar] [CrossRef] [Green Version] - Gimenez, L.; Robert, S.; Suard, F.; Zreik, K. Automatic reconstruction of 3D building models from scanned 2D floor plans. Autom. Constr.
**2016**, 63, 48–56. [Google Scholar] [CrossRef] - Bahar, Y.N.; Pere, C.; Landrieu, J.; Nicolle, C. A thermal simulation tool for building and its interoperability through the building information modeling (BIM) platform. Buildings
**2013**, 3, 380–398. [Google Scholar] [CrossRef] [Green Version] - Kamel, E.; Memari, A.M. Review of BIM’s application in energy simulation: Tools, issues, and solutions. Autom. Constr.
**2019**, 97, 164–180. [Google Scholar] [CrossRef] - Li, H.X.; Ma, Z.L.; Liu, H.X.; Wang, J.; Al-Hussein, M.; Mills, A. Exploring and verifying BIM-based energy simulation for building operations. Eng. Constr. Archit. Manag.
**2020**, 27, 1679–1702. [Google Scholar] [CrossRef] - Spiridigliozzi, G.; Pompei, L.; Cornaro, C.; Santoli, L.D.; Bisegna, F. BIM-BEM Support Tools for Early Stages of Zero-Energy Building Design; IOP Conference Series: Materials Science and Engineering; IOP Publishing: Bristol, UK, 2019; Volume 609, p. 072075. [Google Scholar]
- Salimzadeh, N.; Vahdatikhaki, F.; Hammad, A. BIM-based surface-specific solar simulation of buildings. ISARC. In Proceedings of the 35th International Symposium on Automation and Robotics in Construction, Berlin, Germany, 20–25 July 2018; IAARC Publications: Berlin, Germany, 2018; Volume 35, pp. 889–896. [Google Scholar]
- Salimzadeh, N.; Vahdatikhaki, F.; Hammad, A. Parametric modeling and surface-specific sensitivity analysis of PV module layout on building skin using BIM. Energy Build.
**2020**, 216, 109953. [Google Scholar] [CrossRef] - Dimyadi, J.A.W.; Spearpoint, M.J.; Amor, R. Generating Fire Dynamics Simulator geometrical input using an IFC-based building information model. J. Inf. Technol. Constr.
**2007**, 12, 443–457. [Google Scholar] - Laakso, M.; Kiviniemi, A.O. The IFC standard: A review of history, development, and standardization. J. Inf. Technol. Constr.
**2012**, 17, 134–161. [Google Scholar] - Eastman, C.; Teicholz, P.; Sacks, R.; Liston, K. BIM Handbook: A Guide to Building Information Modeling for Owners, Managers, Designers, Engineers, and Contractors; John Wiley Sons, Inc.: Hoboken, NJ, USA, 2008. [Google Scholar]
- Kim, H.; Shen, Z.; Kim, I.; Kim, K.; Stumpf, A.; Yu, J. BIM IFC information mapping to building energy analysis (BEA) model with manually extended material information. Autom. Constr.
**2016**, 68, 183–193. [Google Scholar] [CrossRef] - Deng, Y.; Cheng, J.C.; Anumba, C. Mapping between BIM and 3D GIS in different levels of detail using schema mediation and instance comparison. Autom. Constr.
**2016**, 67, 1–21. [Google Scholar] [CrossRef] - Zhu, J.; Tan, Y.; Wang, X.; Wu, P. BIM/GIS integration for web GIS-based bridge management. Ann. GIS
**2021**, 27, 99–109. [Google Scholar] [CrossRef] [Green Version] - Liu, Z.; Chen, K.; Peh, L.; Tan, K.W. A feasibility study of building information modeling for green mark new non-residential building (NRB): 2015 analysis. Energy Procedia
**2017**, 143, 80–87. [Google Scholar] [CrossRef] - Lu, Y.; Wu, Z.; Chang, R.; Li, Y. Building Information Modeling (BIM) for green buildings: A critical review and future directions. Autom. Constr.
**2017**, 83, 134–148. [Google Scholar] [CrossRef] - Lappalainen, K.; Valkealahti, S. Effects of PV array layout, electrical configuration and geographic orientation on mismatch losses caused by moving clouds. Sol. Energy
**2017**, 144, 548–555. [Google Scholar] [CrossRef] - Rodrigo, P.; Velázquez, R.; Fernández, E.F.; Almonacid, F.; Pérez-Higueras, P.J. Analysis of electrical mismatches in high-concentrator photovoltaic power plants with distributed inverter configurations. Energy.
**2016**, 107, 374–387. [Google Scholar] [CrossRef] - International Organization for Standardization. ISO TC184/SC4, ISO 10303-11:1994. In Industrial Automation Systems and Integration—Product Data Representation and Exchange—Part 11: Description Methods: The EXPRESS Language Reference Manual; International Organization for Standardization: Geneva, Switzerland, 1994. [Google Scholar]
- Angelis-Dimakis, A.; Biberacher, M.; Dominguez, J.; Fiorese, G.; Gadocha, S.; Gnansounou, E.; Guariso, G.; Kartalidis, A.; Panichelli, L.; Pinedo, I.; et al. Methods and tools to evaluate the availability of renewable energy sources. Renew. Sustain. Energy Rev.
**2011**, 15, 1182–1200. [Google Scholar] [CrossRef] - Catita, C.; Redweik, P.; Pereira, J.; Brito, M.C. Extending solar potential analysis in buildings to vertical facades. Comput. Geosci.
**2014**, 66, 1–12. [Google Scholar] [CrossRef] - Dubayah, R. Estimating net solar radiation using Landsat Thematic Mapper and digital elevation data. Water Resour. Res.
**1992**, 28, 2469–2484. [Google Scholar] [CrossRef] - Fu, P.; Rich, P.M. Design and Implementation of the Solar Analyst: An Arcview Extension for Modeling Solar Radiation at Landscape Scales. In Proceedings of the Nineteenth Annual ESRI User Conference, San Diego, CA, USA, 26–30 July1999; Volume 1, pp. 1–31. [Google Scholar]
- Hay, J.E. Calculation of monthly mean solar radiation for horizontal and inclined surfaces. Sol. Energy
**1979**, 23, 301–307. [Google Scholar] [CrossRef] - Hofierka, J.; Zlocha, M. A New 3-D solar radiation model for 3-D city models. Trans. GIS
**2012**, 16, 681–690. [Google Scholar] [CrossRef] - Iqbal, M. An Introduction to Solar Radiation; Academic Press: New York, NY, USA, 1983. [Google Scholar]
- Kumar, L.; Skidmore, A.K.; Knowles, E. Modelling topographic variation in solar radiation in a GIS environment. Int. J. Geogr. Inf. Sci.
**1997**, 11, 475–497. [Google Scholar] [CrossRef] - Liu, B.Y.H.; Jordan, R.C. The interrelationship and characteristic distribution of direct, diffuse and total solar radiation. Sol. Energy
**1960**, 4, 1–19. [Google Scholar] [CrossRef] - Perez, R.; Seals, R.; Ineichen, P.; Stewart, R.; Menicucci, D. A new simplified version of the Perez diffuse irradiance model for tilted surfaces. Sol. Energy
**1987**, 39, 221–231. [Google Scholar] [CrossRef] [Green Version] - Carl, C. Calculating Solar Photovoltaic Potential on Residential Rooftops in Kailua Kona, Hawaii; University of Southern California: Los Angeles, CA, USA, 2014. [Google Scholar]
- Lukač, N.; Žlaus, D.; Seme, S.; Žalik, B.; Štumberger, G. Rating of roofs’ surfaces regarding their solar potential and suitability for PV systems, based on LiDAR data. Appl. Energy
**2013**, 102, 803–812. [Google Scholar] [CrossRef] - Lukač, N.; Žalik, B. GPU-based roofs’ solar potential estimation using LiDAR data. Comput. Geosci.
**2013**, 52, 34–41. [Google Scholar] [CrossRef] - Zheng, Y.; Weng, Q. Assessing solar potential of commercial and residential buildings in Indianapolis using LiDAR and GIS modelling. In Proceedings of the 2014 Third International Workshop on Earth Observation and Remote Sensing Applications (EORSA), Changsha, China, 11–14 June 2014; IEEE: Piscataway, NJ, USA, 2014; pp. 398–402. [Google Scholar]
- Duffie, J.A.; Beckman, W.A. Solar Engineering of Thermal Processes; Wiley: New York, NY, USA, 1991. [Google Scholar]
- Kreith, F.; Kreider, J.F. Principles of Solar Engineering; Hemisphere Publishing Corp.: Washington, DC, USA, 1978. [Google Scholar]
- Gul, M.S.; Muneer, T.; Kambezidis, H.D. Models for obtaining solar radiation from other meteorological data. Sol. Energy
**1998**, 64, 99–108. [Google Scholar] [CrossRef] - Kasten, F.; Czeplak, G. Solar and terrestrial radiation dependent on the amount and type of cloud. Sol. Energy
**1980**, 24, 177–189. [Google Scholar] [CrossRef] - Cartwright, T.J. Modeling the World in a Spreadsheet: Environmental Simulation on a Microcomputer; Johns Hopkins University Press: Baltimore, MD, USA, 1993. [Google Scholar]
- Gates, D.M. Biophysical Ecology; Springer: New York, NY, USA, 1980. [Google Scholar]
- Schallenberg-Rodríguez, J. Photovoltaic techno-economical potential on roofs in regions and islands: The case of the Canary Islands: Methodological review and methodology proposal. Renew. Sustain. Energy Rev.
**1980**, 20, 219–239. [Google Scholar] [CrossRef] - Panasonic. Solar Power Generation System for Residential Use. Homes and Living. 2014. Available online: http://sumai.panasonic.jp/catalog/solar.html (accessed on 1 May 2021).
- Yuan, J.; Farnham, C.; Emura, K.; Lu, S. A method to estimate the potential of rooftop photovoltaic power generation for a region. Urban Clim.
**2016**, 17, 1–19. [Google Scholar] [CrossRef] - Carneiro, C.; Morello, E.; Desthieux, G.; Golay, F. Urban Environment Quality Indicators: Application to Solar Radiation and Morphological Analysis on Built Area. In Proceedings of the 3rd WSEAS International Conference on Visualization, Imaging and Simulation. World Scientific and Engineering Academy and Society (WSEAS), Faro, Portugal, 3–5 November 2010; pp. 141–148. [Google Scholar]
- NASA. 2019. Available online: https://ladsweb.modaps.eosdis.nasa.gov/ (accessed on 5 July 2020).
- Gimenez, L.; Hippolyte, J.L.; Robert, S.; Suard, F.; Zreik, K. Review: Reconstruction of 3D building information models from 2D scanned plans. J. Build. Eng.
**2015**, 2, 24–35. [Google Scholar] [CrossRef] - Volk, R.; Stengel, J.; Schultmann, F. Building Information Modeling (BIM) for existing buildings—Literature review and future needs. Autom. Constr.
**2014**, 38, 109–127. [Google Scholar] [CrossRef] [Green Version] - Ledoux, H.; Meijers, M. Extruding building footprints to create topologically consistent 3D city models. In Urban and Regional Data Management. UDMS Annual; Krek, A., Rumor, M., Zlatanova, S., Fendel, E., Eds.; CRC Press: Boca Raton, FL, USA, 2009; pp. 39–48. [Google Scholar]

**Figure 1.**Flow chart depicting the overall photovoltaic energy generation potential and calculation methodology presented in this study.

**Figure 5.**Diagrams before and after extraction of the external surface of the building: (

**a**) Industry Foundation Classes (IFC) data of a floor and (

**b**) extracted external surface.

**Figure 6.**Steps to merge the contours: (

**a**) building model and contours; (

**b**) building contours; and (

**c**) merged contours.

**Figure 7.**Steps to determine the grid center points on the facades of a building: (

**a**) build grid center points on the line segment; (

**b**) loop through the contour nodes to build grid center points; (

**c**) obtain grid center points.

**Figure 8.**Classification of type and feasibility of photovoltaic panel area represented by the grid center points: (

**a**) diagram of installation of photovoltaic panels in the windows area; (

**b**) diagram of feasible position of photovoltaic panel in the windows area; (

**c**) diagram of infeasible position of photovoltaic panel.

**Figure 9.**Construction of the roof plane: (

**a**) contour lines of the building; (

**b**) building areas surrounded by contour lines; and (

**c**) Boolean subtraction of areas surrounded by contour lines to obtain the roof height.

**Figure 10.**Construction of grid center points on a roof plane: (

**a**) fishnet and respective center points; (

**b**) grid completely covered by the roof plane and respective center points (blue highlighted area); and (

**c**) grid center points on the roof planes.

**Figure 13.**Building planning and design drawings: (

**a**) design drawings for selected building’s south-facing facade as an example and (

**b**) design drawings for the entire community.

**Figure 16.**Simulation diagram of photovoltaic energy generation in (

**a**) spring (from 21 March to 21 June), (

**b**) summer (from 22 June to 22 September), (

**c**) autumn (from 23 September to 21 December), (

**d**) winter (from 22 December to 20 March) and (

**e**) throughout the year (annual).

**Figure 17.**Available facade and roof areas of the simulated building and the photovoltaic energy generation in different time periods.

**Figure 18.**Schematic comparison of different experimental methods: (

**a**) Industry Foundation Classes (IFC) data method of this study (automated conversion from CAD to 3D BIM); (

**b**) three-dimensional (3D) direct extruding model (source: LOD1 3D model from cadastral data); (

**c**) high-definition image (source: Google Earth); and (

**d**) point-cloud-based method (source: UAV tilt photogrammetry).

**Figure 19.**Functional relationship between the (

**a**) grid resolutions and (

**b**) integration time interval and the computational cost.

Building Element | Actual Area (m^{2}) | Industry Foundation Classes (IFC) Extraction Area (m^{2}) | Available Installation Area (m^{2}) | Proportion of Available Area (%) |
---|---|---|---|---|

Roof | 783.48 | 783.48 | 755 | 96.36 |

Facade (excluding windows) | 17,399.79 | 17,399.79 | 6659 | 38.27 |

Windows | 2726.37 | 2726.37 | 1581 | 57.99 |

Entire building (excluding windows) | 18,183.27 | 18,183.27 | 7414 | 40.77 |

Entire building | 20,909.64 | 20,909.64 | 8995 | 43.02 |

Time Period | Photovoltaic Energy Generation (MWh) | Time Period | Photovoltaic Energy Generation (MWh) |
---|---|---|---|

January | 38.51 | October | 91.45 |

February | 59.55 | November | 53.79 |

March | 97.55 | December | 35.48 |

April | 120.59 | Spring | 342.99 |

May | 102.78 | Summer | 346.93 |

June | 119.94 | Autumn | 194.51 |

July | 122.22 | Winter | 170.25 |

August | 116.23 | Annual | 1054.69 |

September | 99.84 |

Method | Semantic Information | Data Acquisition Difficulty | Accuracy | Information Integrity | Extracted Building Elements |
---|---|---|---|---|---|

Industry Foundation Classes (IFC) | Yes | Medium | High | High | Roof, Facade, Window |

Direct extruding | No | Easy | Low | Low | Roof, Facade |

High-definition image | No | Medium | Low | Low | Roof |

Point cloud data | No | Difficult | High | Medium | Roof, Facade |

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## Share and Cite

**MDPI and ACS Style**

Lu, X.; Li, G.; Wang, A.; Xiong, Q.; Lin, B.; Lv, G.
Estimating the Photovoltaic Potential of Building Facades and Roofs Using the Industry Foundation Classes. *ISPRS Int. J. Geo-Inf.* **2021**, *10*, 827.
https://doi.org/10.3390/ijgi10120827

**AMA Style**

Lu X, Li G, Wang A, Xiong Q, Lin B, Lv G.
Estimating the Photovoltaic Potential of Building Facades and Roofs Using the Industry Foundation Classes. *ISPRS International Journal of Geo-Information*. 2021; 10(12):827.
https://doi.org/10.3390/ijgi10120827

**Chicago/Turabian Style**

Lu, Xiu, Guannan Li, Andong Wang, Qingqin Xiong, Bingxian Lin, and Guonian Lv.
2021. "Estimating the Photovoltaic Potential of Building Facades and Roofs Using the Industry Foundation Classes" *ISPRS International Journal of Geo-Information* 10, no. 12: 827.
https://doi.org/10.3390/ijgi10120827