A rapid evaluation method of existing building applied photovoltaic (BAPV) potential

Energy and Buildings 135 (2017) 39–49

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A rapid evaluation method of existing building applied photovoltaic (BAPV) potential Zhang Wen, Zhang Yukun, Li Zhe, Zheng Zheng, Zhang Rui ∗ , Chen Jiaxuan School of Architecture, Tianjin University, Tianjin 300072, China

a r t i c l e

i n f o

Article history: Received 4 August 2016 Received in revised form 23 October 2016 Accepted 8 November 2016 Available online 10 November 2016 Keywords: Rapid evaluation of PV potential Existing building applied photovoltaic Geometrical information acquisition in buildings Imagebased 3-dimensional (3D) reconstruction

a b s t r a c t By analyzing and making a comparison between five methods of geometrical information gathering in existing buildings, this paper shows that the method of geometrical information gathering in buildings based on the 3-dimensional (3D) geometrical reconstruction is suitable for the evaluation of existing building applied photovoltaic (BAPV) potential. Then, it tested the accuracy of different ways of image acquisition by using low-rise, multi-storey and high-rise buildings as experimental subjects. Moreover, based on a building in Tianjin, an evaluation method is proposed to verify the feasibility and accuracy of the method. Finally, we developed a reliably and feasibly evaluation method to evaluate the BAPV potential. © 2016 Elsevier B.V. All rights reserved.

1. Introduction At present, nearly half of the world’s population (about 3.5 billion people) live in cities, and use more than 80% of the world’s energy. It is expected that by 2030, nearly 60% of the world’s population (about 5 billion people) will live in urban areas [1], and the world’s total energy consumption will also increase another 37% than that in the current [2]. Moreover, in the next few decades, 95% of all urban expansion will occur in developing countries [1]. If we apply the trends found in urbanization in the developed world to urbanization in the developing world, then it is clear that the consumption of building materials as well as the energy requirements in these areas will increase dramatically year over year [3]. Thus it is crucial for existing buildings to reduce their energy demands to the greatest extent possible [4]. Moreover, it is extremely important for those buildings to use a renewable resource such as solar energy as their primary energy source due to its safety, cleanness and significant energy generating potential [5]. There will be a growing trend for cities to adopt photovoltaic (PV) power for their energy supply; adopting solar energy is also an important strategy in solving the energy crisis in cities [6]. PV has been used on a large scale on existing urban buildings in Germany, the United States, Japan, China and other countries.

∗ Corresponding author. E-mail address: zhangrui [email protected] (R. Zhang). http://dx.doi.org/10.1016/j.enbuild.2016.11.012 0378-7788/© 2016 Elsevier B.V. All rights reserved.

Those countries have developed plans to adapt PV to the national conditions [7,8]. These developments make it clear that our work on existing building applied photovoltaic (BAPV) potential is likely to be in high demand around the world. From a macro perspective, assessment on BAPV potential can provide a theoretical basis for governments to formulate viable photovoltaic development goals. Municipalities can use our work in developing their own energy regulations designed to optimize the use of energy in their cities. From the microcosmic point of view, the study of BAPV potential will not only be helpful to the energy-saving transformation of existing building, it will also provide data to support to the promotion of new energy applications. Therefore, the study of BAPV potential has attracted increasing attention all over the world. When determining the BAPV potential, the most important step is calculating the available area of the building envelope. We need to collect the geometrical information about the area available for PV modules on envelope structures. There is already a considerable amount of research about the potential of applying PV on rooftops [9–14,22–29,31,43–45], but research about applying PV to building facades only began a few years ago [15–21,30,40]. Based on the urban planning parameters and original drawings, statistic information about the potential for rooftop photovoltaic systems were collected with the stratified sampling method in paper [9,31]. In paper [15], the authors used sampling methods to evaluate the photovoltaic potential evaluation. Geometrical information of building samples was gathered by original architectural drawings, while the rest were calculated by statistical method. In [30], informa-

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W. Zhang et al. / Energy and Buildings 135 (2017) 39–49

tion acquisition was achieved by retrieving drawings and manual measurement. Potential calculation was conducted through a combination with Sketchup, Ecotect and Pvsyst software. The advantage of this method is high accuracy, but the computational cost is very high and the operation are cumbersome. In [12], the existing building roof area was obtained through a combination of Geographic Information System (GIS) and orthophoto map. In [22,14], with the roof area extracted form highly accurate orthophoto map, the potential calculation was carried out with the roof photovoltaic available coefficient. This method has the advantages of high speed, but it can only obtain the geometrical information of the roof, not the complete building envelope. In recent years, a new method, with a combination of Light Detection and Ranging (LiDAR), remote sensing images and GIS has been used for geometrical information acquisition [16–21,23–29] and has been widely used in the BAPV evaluation; this greatly improves the accuracy of acquired information. Among them, [16] is representative, in which LiDAR data was transformed into a digital surface model (DSM). In combination with the meteorological data, the photovoltaic available area on fac¸ade was calculated directly in GIS and then the BAPV potential calculations were carried out. However, the method is more demanding for hardware equipment and the cost of equipment is relatively high. In [9–31,40], different geometrical information acquisition methods for existing urban buildings are listed; these records differ in economy, society, and accuracy. The main reason for these differences lies in the area of concern and the scale of the calculation. In [9,15,17,32–34], the geometrical information acquisition method is more likely to be applicable for the scale of a city, and it is not typical for individual buildings or smaller geographic area. The geometrical information acquisition method is based on orthophoto image; it is unable to acquire complete geometric information. The method based on LiDAR and original drawings, etc. can completely obtain the information of existing construction monomers and do so with higher accuracy. Overall, for the purpose of BAPV potential evaluation, the existing building geometrical information acquisition methods described above are complicated to operate and are unable to meet the requirements of speed and accuracy. At the same time, LiDAR is a quick way to obtain information but it is not universally applicable due to its high cost (see Section 2.1 for more details). Therefore, at the scale of an individual building, we are putting forward a new method which can meet the demands of both speed and accuracy. It will serve as a springboard for future research on information acquisition regarding the application of large scale urban and regional photovoltaic potential. The purpose of this paper is to develop a brand-new way to collect geometrical information suitable for all types of existing buildings. In this paper we test its accuracy and prove its reliability is proved so that it can be used to calculate the actual capacity in PV applications. This paper is divided into five chapters. In the second chapter, several common building information acquisition methods are compared, and the method of image-based 3-dimensional (3D) reconstruction is summarized and introduced. In the third chapter, the acquisition methods of geometrical information on different architectural scales are tested. In the fourth chapter, geometrical information acquisition methods which are eventually applied to different scales of buildings are summarized. An example will be given to test the feasibility and efficiency of the method based on 3D geometrical reconstruction for BAPV potential evaluation. The final chapter summarizes the paper. 2. Methodology First, several common ways of geometrical information acquisition in existing buildings are compared. Then, a way to acquire

geometrical information that is suitable for the evaluation of BAPV potential is screened out from several perspectives; our screening included including cost, time consumption, specialization and applicability. Secondly, the universality of the method is verified in different architectural scales. The analysis and comparison of geometrical information errors for different architectural scales confirm that it is suitable at different architectural scales. Last, but not least, we evaluate the PV application potential of a typical existing building and we record the time required for the evaluation to ensure the viability of the implementation of the method. 2.1. Comparison among various ways of acquiring geometrical information of existing buildings There are several common ways of acquiring information about existing buildings: original drawing, orthophotoquad, a network-based interactive platform, LiDAR, and image-based 3D reconstructions. The use of original drawings is the most common way in the study of BAPV potential [6,11,30]. This way has the highest accuracy, but it also has some problems; it is time-consuming and retrieving the drawings is complicated. Besides, the drawings of many existing buildings are simply not available. Orthophotoquad or satellite maps is useful in acquiring geometrical information about existing buildings [9,12,14,22]. The advantage here is that it is fast and easy to access the geometrical information. Generally speaking, the geometrical information obtained this way only includes information about the buildings’ roofs, but it can’t satisfy the demand for information about the existing buildings’ facade potential, thus it is impossible to complete the evaluation of BAPV potential. The network interactive platform acquires geometrical information for the BAPV potential evaluation [17,32–34]. This method collects information by communicating directly with building owners or building users through the network platform. The advantages include high speed and comprehensive information acquisition; it also takes the local electricity price and cost recovery period into account. On the other hand, the disadvantage is its relatively poor accuracy. LiDAR is widely applied to mapping historic buildings with the recent advancements in laser technology [35–38]. There are some research achievements in the study of BAPV potential at the urban scale [16–21,23–29]. The advantages here include high speed, stronger initiative, centimeter-sized accuracy and timeliness [17]. However, this method of information acquisition also has some defects such as extremely high costs and a heavy demand for professional operation. In addition, since LiDAR handles information acquisition with polar coordinates, it may have a relatively rigorous requirement for the angle and distance of the scanned area. In other words, it is limited to acquiring information about the facade of low-rise buildings, it cannot acquire information about the top surfaces of buildings. When information about the top surface of high-rise buildings is required, it is necessary to resort to unmanned aerial vehicles or other devices. According to different algorithms, image-based 3D reconstruction can be divided into 3D point cloud reconstruction and 3D geometrical reconstruction. In light of different image sources, these ways can be subdivided into image-based 3D reconstruction of ground photography and image-based 3D reconstruction of lowaltitude photography. The research shows that image-based 3D reconstruction is more suitable for geometrical information acquisition in the study on the BAPV potential, because consideration should be given to safety in the application of PV modules on the elevations. In other words, when PV modules are applied to building elevations, these modules should not be used on the building elevation below 3 m because it is easy to damage PV modules. Fur-

W. Zhang et al. / Energy and Buildings 135 (2017) 39–49

41

Fig. 1. The Analysis of Applicability for image-based 3D reconstruction.

thermore, PV applications on building elevation also need to take into account the shade of the surroundings such as border trees and buildings. Undoubtedly, there is no need to acquire geometrical information of blocked areas. Such a situation happens to be propitious to image-based 3D reconstruction because images of the blocked areas often are unable to be acquired as shown in Fig. 1. As a result, the advantages of image-based 3D reconstruction lie in faster acquisition and higher accuracy, thus this way is very suitable for studying BAPV potential. What’s more, it even can achieve centimeter-level precision. However, the method also has some disadvantages, such as the iterative calculation of point cloud and the time required to collect the data, and so on. To sum up, compared with other ways of acquiring information, the way based on 3D image reconstruction is suitable for evaluation of BAPV potential comprehensively from the perspective of time consumption and costs. 2.2. Software selection and algorithm analysis The image modeling software named ImageModeler® , developed jointly by AUTODESK and REALVIZ, to test the method of information acquisition from existing buildings via image-based 3D reconstruction. Based on extracting building information from digital images, the algorithm establishes a matching relationship of features for multiple digital images in the form of man-machine interaction, demarcates cameras based on the matching relationship, and then reconstructs a 3D geometrical model. Moreover, a mathematical model is utilized to deepen our understanding of the acquisition process of digital images. As a matter of fact, four different coordinates are established within the different spatial categories including world coordinate system, camera coordinate system, 2dimensional (2D) image coordinate system and imaging coordinate system. The point P in the world coordinate system is converted to the point P’ in the imaging coordinate system by three different matrix equation conversions. The following figure illustrates how a picture can be used to calculate the coordinates of the point P in the world coordinate system. However, that Point P can move arbitrarily along the line from the center of the camera, C, to the point P, as shown in the picture on the left hand side of Fig. 2. In other words, it is impossible to acquire the 3D spatial coordinates of point P by using only

one picture without other constraint conditions. In this situation, it is unable to complete the 3D model reconstruction of buildings. As shown in the picture on the right hand side of Fig. 2, P1 is matched with P2, and the imaging centers of a camera are C1 and C2, respectively, thus point P in space must be located in the prolonged point of intersection between C1 p1 and C2 p2 and has a unique solution. It can be observed that at least two picture are necessary to be calculated, for the sake of acquiring coordinates of feature points. In the actual calculation, an increasing number of pictures will be required to enhance the corresponding calculations. For example, if m pictures are introduced, the process of calculating matching 2 times. Thus, increasing the number of pictures points should be Cm will improve the matching process and, in turn, reduce the image errors and generate more accurate matching results. The “Euclidean Self-Calibration with the Modulus Constraint” [39] proposed by Professor Pollefeys in 2002 can revise camera parameters, including the Principal Point, Focal Length, Filmback Heigth/Filmback Width, and Non-linear Distortion, for non-metric cameras. Namely, the project matrix is solved by extracting and matching feature points. The relationship between the projection matrix and the feature points is used to solve internal and external parameters of a camera inversely and finish the self-demarcation process of a camera. Finally, marked camera results are used to complete the rematch of stereo image pairs and determine the depth of the points, namely the 3D coordinates of feature points and image points in the world coordinate system. Described by mathematical linguistics, point P1 and point P2 are 3D homogeneous coordinates p1 = (u, v, 1) and p2 = (u , v , 1) in two corresponding image coordinates for point P in the world coordinate system. Coordinates of these two points conform to the antipode constraint  u conditions respectively and are converted to (u, v, 1) * v 1 As shown in the following Formula (1), it can be observed that the matrix row in the equation is changed into vectors of (uu , uv , u, vu , vv , v, u , v , 1). The unique solution of basis matrix F can be acquired under the condition of being short of one constant factor.

⎡

f11

f12

f13

F = ⎣ f21

f22

f23

f31

f32

f33

⎢

⎤ ⎥ ⎦

Basis Matrix F

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W. Zhang et al. / Energy and Buildings 135 (2017) 39–49

Fig. 2. Point Matching in the World Coordinate System.









uu f11 + uv f21 + uf31 + vu f12 + vv f22 +vf32 + u f13 + v f23 + f33 = 0

(1)

Therefore, at least 8 linear systems of equations are required to solve for the unique solution for the matrix. In other words, at least 8 pairs of matching feature points are selected in the actual feature matching process to demarcate and establish 8 linear systems of equations. Then, the iterative computation of the matrix is used to solve for the unique solution of F matrix, namely the abovementioned n equations are overlapped and are converted to the following systems of equations. As shown in Formula (2), if F needed to be solved, 8 unknown numbers in the Formula (1) at least should solve by using eight systems of equations.

⎡ u u , v u , u , u v , v v , v u , v , 1 1 1 1 1 1 1 1 1 1 1 1 1 ⎢. ⎢ Af = ⎢ ⎢. ⎣.

⎤ ⎥ ⎥ ⎥f = 0 ⎥ ⎦

(2)

un un .vn un , un , un vn , vn vn , vn , un , vn , 1 Therefore, a nonzero parameter can be included into a matrix with 8 unknown numbers. However, the camera lens may not belong to pinhole camera components under the idea situation, so lens distortion may be presented. As a result, the way of setting up more feature points in realistic operations should be applied to reduce the influences of instrumental errors. More feature point pairs may be introduced for calculation, but only 8 points will be selected from n points to calculate. In other words, the calculation process will be Cn8 times. Moreover, its result F will be utilized to solve for the average with the condition of n ≥ 8, so as to reduce the error. The conclusion can be draw as follows: at least 8 pairs of feature matching points are required. It needs to match with more matching points for the sake of confirming the matrix F and completing the demarcation of camera parameters. For this reason, at least two digital images and 8 points are required to complete the demarcation process. After completing the camera demarcation, images may conduct reprojection in line with the camera’s parameters, obtain new image pairs and solve for the 3D coordinates of points in line with the computing equation in the Formula (2), complete 3D model reconstruction, and finish the acquisition of the geometrical information of the buildings. 2.3. Verification methods to Be tested Because the information acquisition technology in buildings based on 3D geometrical modeling is based on the modeling of digital images, the ways of shoot for various building scales are also different. This means, there are limitations when gathering information from these images. As shown in Fig. 1, if a high-rise building is shot from the perspective of humans, pixel points on the top area

of the building may form unclear feature points because they are out of focus, so it is impossible to complete the matching of feature points. Thus, it will cause a modeling error. On the other hand, if it is shot remotely, the error formed by dense pixel points on the surface can be reduced, and the bottom areas are greatly impacted by their surroundings. For this reason, it is impossible to calculate the BAPV potential area accurately. In addition, if the way of low-altitude shoot is adopted, the information about the entire building can be acquired from the air in the form of aerial photos from unmanned aerial vehicles, so as to depict the building more comprehensively. However, shooting costs caused by unmanned aerial vehicles are relatively high, so it is not cost-effective for small scale buildings. As previously discussed, this paper presents an image acquisition method with simple operation, high accuracy and general applicability, which meet the requirement of evaluation for BAPV potential. The ways of image acquisition for buildings with different scales were introduced in this paper. There is a comparison between CAD and data from different modes of conducting 3D geometrical reconstruction to acquire a one-dimensional standard deviation and obtain image information acquisition technology from buildings at different scales. The computational formula of SD was shown as follows:

␪i =

vi ui

 n   i 2

× 100%; ␦ =

i=1

n

Where n is the data size, vi is the difference value between the measured data and CAD(ui ), ␪i is the percentage error magnitude, and ␦ is the standard percentage difference. The standard deviation for comparison was 5%, and can be attributed to the following causes: First of all, PV modules are usually presented in the form of a PV panel in BAPV. The common frame size of the PV panel is 5 cm. Moreover, the PV panel isn’t filled with PV materials. Secondly, the influences of the shield should be considered in the process of calculating BAPV potential. In the study of the BAPV potential, the statistics of available about the PV areas may not be recorded accurately. Thirdly, in the calculation of BAPV potential, the PV parameters are empirical values, so the requirements for the accuracy of the building information may not be as rigorous as required in building surveying. Thus, 5%, which means that there is a 5 cm error in 1 m, is an acceptable error in BAPV potential evaluation. 3. The experimental measurement of different building types The test aimed at confirming appropriate ways of image acquisition applied to buildings built at various scales. Existing buildings was classified into low-rise buildings, multi-storey buildings and high-rise buildings in terms of their height. Low-rise buildings referred to buildings that were less than or equal to 10 m in height;

W. Zhang et al. / Energy and Buildings 135 (2017) 39–49 Table 1 Information Errors of Low-rise Buildings. Axial direction

Scantling

Test Data(m)

CAD (m)

Difference vi(error) (m)

South

a1 c1 a2 c3 c4 a3 c2

10.41 3.55 2.92 2.19 1.82 1.16 1.35

10.15 3.47 2.96 2.3 1.8 1.14 1.4

0.26 0.08 −0.04 −0.11 0.02 0.02 −0.05

East

b1 b2 c5

7.59 1.28 2.4

7.31 1.27 2.3

0.28 0.01 0.1

multi-storey buildings referred to buildings that are higher than 10 m but lower than or equal to 24 m; high-rise buildings referred to buildings that are higher than 24 m. (1) A typical low-rise building was chosen as a test object. Pictures were taken from the ground, as shown in Figs. 3 and 4. Images inputting were demarcated and measured. The data analysis was shown as Table 1. According to the formula mentioned above, it could be observed that a standard deviation of 4.88% < 5%. The standard deviation was kept in an acceptable range, so we concluded that ground photography could be used for acquiring building information in low-rise buildings. It took 5 min to take photos. It took another 10 min in interior work to reconstruct 3D model and acquire geometrical information. Therefore, the whole process required a total of 15 min. (2) In order to test the methods with multi-storey buildings, a six-storey office building in Tianjin was selected as the test object. Photos of the east side and south side, which have better conditions for BAPV, were taken from the ground from four different per-

43

spectives. Then, images were inputted for measurement, as shown in Fig. 5. On the other hand, for low-altitude aerial photography, unmanned aerial vehicles graciously provided by the low-altitude aerial information acquisition research group of Tianjin University were applied two different routes with 30◦ and 45◦ angle of depression, respectively, to take pictures. The software was guided into it to measure, as shown in Fig. 6. Finally, data were compared in the paper, as shown in Table 2, so as to look for a suitable way of image acquisition for multi-storey buildings. After data were substituted into the formula and calculated, it could be observed image-based 3D reconstruction based ground photography had a standard deviation of 5.01% while for lowaltitude photography it was 4.04%. The standard deviations of both methods fall within an acceptable range. In light of standard deviations, low-altitude photography will lead to a more accurate result, but photos taken from the ground can be used as an acceptable alternative. In terms of time consumption, the time required to take the photos from the ground took 15 min, mainly because it took some time to look for the most suitable shooting angle. Time consumption of office work was 15 min, because more data should be processed. The total time consumption was 30 min. On the other hand, lowaltitude photography required 5 min of field work, since unmanned aerial vehicles fly quickly. After the routes, the take-off site and landing site were selected, shooting was completed smoothly. Time consumption of industrial data processing was about 15 min. The total time consumption was 20 min. To sum up, low-altitude photography may save some time looking for a suitable site due to the flexibility of air routes, shooting angles, and fewer other constraints. (3) A ten-storey building with a height of 42 m in Tianjin was selected as a testing object for the test of high-rise buildings. Photos of the east side and south side of the building’s surface, which have better conditions for BAPV, were taken from the ground and low-

Table 2 Different Ways of Information Acquisition in Multi- storey Buildings. Axial direction

Scantling

Acquisition of ground photography(m)

Acquisition of low-altitude photography (m)

CAD (m)

South elevation

a1 a2 a3 a4 a5 a6 a7 a8 a9 a10 a11 a12

18.80 5.26 1.75 2.83 20.53 20.53 1.75 2.73 2.96 5.97 3.63 14.02

17.97 5.09 1.79 2.78 20.65 20.65 1.79 2.77 2.87 5.87 3.78 12.9

18.00 5.10 1.70 2.80 20.80 20.80 1.70 2.95 3.10 5.20 3.80 13.50

East elevation

b1 b2 b3 b4 b5 b6

21.43 17.95 9.23 38.33 3.55 21.09

18.73 17.26 9.24 38.95 3.43 22.07

18.60 17.70 9.24 39.48 3.60 22.30

South elevation

c1 c2 c3 c4 c5 c6 c7 c8 c9

2.07 2.00 18.03 1.24 1.98 2.08 4.18 2.01 24.10

2.02 1.92 18.08 1.17 1.92 2.06 4.43 1.91 24.27

2.10 2.00 18.10 1.19 2.00 2.00 4.30 2.00 24.30

East elevation South elevation

c10 Angle of parapet

2.79 60.93◦

2.76 62.49◦

2.80 62◦

44

W. Zhang et al. / Energy and Buildings 135 (2017) 39–49

Fig. 3. Two Selected Images.

Fig. 4. Basic Size Measurement of Buildings.

Fig. 5. Four Images Selected in Ground Shoot & Basic Size Measurement of Buildings.

altitude. The method was basically same as that of multi-storey buildings. Take photos from the ground and low-altitude, and then input images into software, so as to acquire geometrical data, as shown in Fig. 7 and Fig. 8. Finally, suitable image acquisition method for high-rise buildings was chosen based on the data analysis as shown in Table 3. After data were substituted into the formula and calculated, it could be observed that when used to collect information of

high-rise buildings, image-based 3D reconstruction of ground photography produced a standard deviation of 7.61%, while the low-altitude photography produced a standard deviation of 3.27%. Since 7.61% » 5% > 3.27%, it suggests that low-altitude aerial photography, if possible, should be used to collect digital images and acquire building information. For the acquisition process of ground photography, it spent 15 min on the field work, and it was also attributed to selecting

W. Zhang et al. / Energy and Buildings 135 (2017) 39–49

Fig. 6. Four Images Selected from Low-altitude Shoot & Basic Size Measurement of Buildings.

Fig. 7. Two Images Selected from Ground Shoot & Basic Size Measurement of Buildings.

Fig. 8. Three Images Selected from Low-altitude Shoot and Basic Size Measure of Buildings.

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46

W. Zhang et al. / Energy and Buildings 135 (2017) 39–49

Table 3 Different Ways of Acquisition Information in High-rise Buildings. Axial direction

Scantling Acquisition of ground photography(m)

Acquisition of low-altitude photography(m)

CAD (m)

South elevation

a1 a2 a3 a4 a5 a6 b1 b2 b3

74.92 31.85 18.22 43.07 121.50 133.77 16.07 23.77 27.13

85.35 34.96 20.14 50.39 131.88 155.35 14.52 23.98 27.07

83.10 32.05 19.05 51.05 131.05 155.05 14.70 23.95 27.10

West Elevation

b4 b5 c1 c2

7.73 19.12 25.07 8.40

7.79 19.12 26.98 8.49

7.80 19.20 27.10 8.20

shooting sites. It spent another 15 min processing the data at the office and the total time consumption added up to 30 min. As for low-altitude photography, the field work took about 5 min. Time consumption of office work was about 15 min and the total time consumption added up to 20 min. Low-altitude photography takes less time than ground photography for high-rise buildings.

take manual measurements if the ground photography of the roof is not inadequate. For multi-storey buildings – buildings which are higher than 10 m, but lower than or equal to 24 m – it can be inferred in Section 3 (2), 5% > ␪groundphotography > ␪low-altitudephotography . Thus low-altitude photography is recommended for its precision. Further, since, the standard deviation of ground photography also falls in an acceptable range, it can be used as an alternative. All in all, if the conditions permit, ground photography should be replaced with aerial photography when the building roofs cannot be seen from the ground. For the high-rise buildings – buildings higher than 24 m – it can be inferred, ␪groundphotography > 5% > ␪low-altitudephotography in Section 3 (3). Ground photography of high rise buildings is inadequate because the feature points are unclear at the tops of the buildings because they are out of focus. This may lead to the mismatched feature points and modeling error. Moreover, ground photography cannot obtain the rooftop information. Therefore, the low-altitude photography is recommanded to obtain the precision required. For the buildings with complex architectural forms or with selfocclusion, such as the T-shaped multi-storey buildings in Section 3 (2), it is not possible to obtain the complete image of main building from one angle. In these cases, photos should be shot with different blocks and from different angles, as shown in Fig. 5. Then the complete geometrical data of the building can be obtained through camera calibration, block synthesis and size measurement.

4. Results and discussions 4.1. Data acquisition and processing of BAPV potential evaluation via image-based 3D reconstruction Based on the above-mentioned introduction and relevant testing analysis of this information acquisition technology and image-based 3D reconstruction, several suggestions regarding the evaluation of BAPV potential with this technology are proposed as follows. (1) The selection of image acquisition methods Images can be acquired by using cameras with prime lens. With lens of a fixed focal length, the spatial information contained in each picture is more accurate, the photo deformation is less, thus the space coordinates calibrated are more accurate. In taking pictures of buildings with different sizes, it is suggested that using different shooting methods for buildings of different scales. Conclusions can be drawn as follows: For low-rise buildings – buildings of 10 m in height or less – it can be inferred in Section 3 (1), 5% > ␪groundphotography . Ground photography should be used in this case. It is entirely sufficient to meet the data acquisition requirements. However, it is necessary to

(2) The photo selection As described in Section 2.2, it is not possible to obtain the unique solution with one or two photos taken with approximate angles. At least two photos with different shooting angles should be chosen as data sources. The accuracy of the results will improve as the calculations are more tightly aligned. Thus using more photos that meet the requirements will improve the evaluation. Pictures cropped or processed by image processing software and data sources obtained by selecting images with different color balance states shouldn’t be used in 3D reconstruction. Image processing technology will modify the graphics data in the photos. This, in turn, will complicate the distortion correction, displace the principle point, and change the focal length of the image. As a result, the images cannot be calibrated correctly; this will lead to larger differences in the photovoltaic potential evaluations. The selected photos should include the main body of the building, and contain as much 3D volume information as possible instead of focusing on details, sections and components of a buildings. Photographs that do not meet these criteria will not be useful in reconstructing the space coordinate system., because they lack adequate depth information.

Table 4 Building Information Acquisition via image-based 3D reconstruction. Dimensioning legend

Size

Test Data(m)

Size

Test Data(m)

a1 c1 a2 c3 c4 a3 c2 b1 b2 c5

10.41 3.55 2.92 2.19 1.82 1.16 1.35 7.59 1.28 2.40

c6 c7 a4 a5 c8 a6 c9 c11 a7 c10

3.92 2.30 1.79 1.20 0.60 0.49 0.79 1.07 7.89 1.07

W. Zhang et al. / Energy and Buildings 135 (2017) 39–49

47

19505.71

737.74 20.78

2166.9 77.66

1890.2 76.12

371 54.89

– –

– –

Monocrystalline silicon 82 41.71 – – Monocrystalline silicon Amorph-ous silicon

Polycrystal -line silicon 1155.1 136.82 Annual (KW h) Generating efficiency (KW h/y/m2 ) Total annual PV potential(KW h)

329 38.97

Roof on the north slope 8.44 Roof on the south slope 8.44 Scantling

Flat roof

Elevation Top surface Axial direction

Table 6 The Simulated Result Statistics of BAPV Based on image-based 3D reconstruction.

4.2. Comparison for evaluation method of BAPV potential A complete process of rapid evaluation of the BAPV potential of existing buildings was tested with a building in Tianjin, China. The calculation process was combined with a rational use of PV modules [41]: the north side of the building was paved with amorphous silicon (a-Si) PV modules, transparent components with monocrystalline silicon (m-Si) PV modules [41]. Other parts were paved with polycrystalline silicon (p-Si) PV modules with high cost performance. In addition, PVSYST software developed at the University of Geneva was used to calculate the PV potential [41,42]. The above-mentioned low-rise building was selected as a testing object. The images of the entire building were taken by ground photography. Not only the south and east sides was selected, as mentioned in Chapter 3, but also the north and west sides. Onedimensional data were summarized in Table 4. Moreover, Table 5 shows the estimated applicable areas. The meteorological parameters of Tianjin city from Special Thermotechnical Meteorological Data of Chinese Buildings were inputted into the software of PVSYST before the calculation to ensure accuracy of PV potential in the computational process. Thus, suitable PV modules were selected to stimulate PV potential and calculate the generating efficiency of different elevations of regional areas, as shown in Table 6. The whole process required about 30 min. Moreover, the test also verified that image-based 3D reconstruction is a workable approach to evaluate BAPV potential. This paper introduced the process of using 3D geometrical reconstruction as the basis for evaluating photovoltaic potential and confirming that it is a rapid and feasible evaluation method.

Polycrystalline silicon

North wall space 35.50

As to camera calibration and dimension measurement, the algorithm discussed in 2.2 made it clear that feature points should be evenly distributed in the image and as close to the main building as possible. Further, these feature points must be strictly matched as homonymy points. Otherwise, the calibration of the camera will be inaccurate because the image will not have sufficient depth. In addition, the number of feature points should be at least 8 pairs. Relatively larger objects close to the main body of building is recommended for the calibration and dimension measurement. To sum up, in the study of the BAPV potential, the method of digital image acquisition should be selected based on a comprehensive analysis. Then, digital images of buildings are chosen in accordance with requirements. Finally building information acquisition through image-based 3D reconstruction is completed by setting up suitable feature point matching and constraint conditions in software to calibrate a camera.

Polycrystalline silicon

(3) Camera calibration and data processing

Amorph -ous silicon

10.561034 5.2241 0 3.072

Polycrystalline silicon 2550.7 96.64

South PV glass North PV glass West PV glass East PV glass

Polycrystalline silicon 10255 143.42

Transparent materials

71.50

26.394466 35.5047 27.9028 24.8308

Radiating area(m2) Battery types

South wall space North wall space West wall space East wall space

West PV glass 0

Elevation

North PV glass 0

8.4423 8.4423 71.50257939

South PV glass 6.76

Roof on the south slope Roof on the north slope Flat roof

East wall space 24.83

Top surface

West wall space 27.90

PV Applied Areas Based on Images(m2)

South wall space 26.39

PV replaceable components

Transparent materials

Areas

East PV glass 1.97

Table 5 PV Applied Area Statistics in Two Ways of Information Acquisition.

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W. Zhang et al. / Energy and Buildings 135 (2017) 39–49

Table 7 Comparison of information acquisition methods for BAPV potential. Method of geometrical information acquisition

Applicable scope

Information acquisition method based on original drawings

All building types with well reserved drawings and available access All building types Low-rise building and multi-storey building (airborne laser scanner is required for high-rise buildings)

Information acquisition method based on manual measurement Information acquisition method based on LiDAR

Image-based 3D reconstruction

3D geometrical reconstruction (ground photography) 3D Point cloud reconstruction (ground photography) 3D geometrical reconstruction (low-altitude photography) 3D Point cloud reconstruction (low-altitude photography)

Based on the earlier experience in obtaining geometrical information for buildings, different methods of information acquisition in existing buildings mentioned in Chapter 2 is compared with image-based 3D reconstruction from the perspective of both time consumption and cost. Time consumption consists of the time to acquire images in field work and the data processing time; the cost means capital expenses during the whole process of the study of BAPV potential evaluation. Obtaining geometrical information about existing buildings from their original drawings was not generally applicable. Without a perfect network database of drawings, it will take a long time to fetch any given drawing; further, many drawings are not available. As for obtaining the geometrical information by manual measurement, the consumed time is varied according to volume of the building mass, the number of mapping staff, the mapping proficiency and the complexity of the building shape. The mapping time will be longer for buildings with larger masses or complicated shapes. The advantage of obtaining information based on 3D point cloud reconstruction lies in its high image capture rate. However, since 3D point cloud reconstruction is based on the scale-invariant feature transform (SIFT) algorithm, namely automatic iterative calculation, the point cloud computing time is relatively long, as a result, the whole process takes a long time. The time required for LiDAR or 3D geometrical reconstruction is relatively short . In terms of cost, using original drawing cost the least of all the information acquisition methods. The cost associated with manual measurement was primarily for manpower, so it varied according to different sized buildings and the cost of labor. The LiDAR method is relatively professional and the measurement devices cost more, compared to low-altitude photography aerobat (the aerobat used in this paper is DJI phantom). The ground photography method requires fewer photos. Images of an off-site building could be obtained with combination of internet transmit, thus the commuting costs is saved. Table 7 summarizes this analysis and our experience in obtaining geometrical information. Compared with other methods to aquire geometrical information, the method based on 3D geometrical reconstruction has the advantages as high speed, high accuracy, low cost and general applicability, etc. 5. Conclusions This paper compared different methods of geometrical information acquisition in existing buildings. A suitable way of collecting geometrical information for the evaluation of the BAPV potential was discussed, namely image-based 3D reconstruction. Testing analysis showed that different ways of image acquisition should be used for different types of buildings. Specifically, ground photography is recommended for low-rise buildings, ground photography and low-altitude photography for multi-storey buildings and low-

Low-rise building and multi–storey geometrically regular buildings Low-rise buildings and multi–storey buildings All building types with regular structures All building types

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