Rooftop Solar Photovoltaic Systems: Comparison
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Please note this is a comparison between Version 2 by Camila Xu and Version 1 by Xiliang Liu.

Rooftop solar photovoltaic (PV) retrofitting can greatly reduce the emissions of greenhouse gases, thus contributing to carbon neutrality. Retrofitting distributed rooftops with solar PV is an effective means of promoting “carbon peaking” and “carbon neutral” strategies. Rooftop solar PV is geographically unrestricted. The PV cells can be closely integrated into buildings without taking up additional land resources, not only saving land resources but also improving their utilization rate.

  • GF-2 remote sensing images
  • building rooftop recognition
  • rooftop solar PV
  • DeepLabv3+
  • MLP
  • carbon reduction

1. Introduction

At present, the primary energy sources globally are non-renewables including coal, oil, and natural gas. With global economic growth, energy demand continues to increase. Huge energy consumption reduces the availability of non-renewable energy, and the energy crisis has become one of the major problems plaguing the world [1]. In addition, the exploitation of fossil energy is a key factor in global environmental change and pollution, bringing serious ecological and environmental problems such as the greenhouse effect and global warming [1,2][1][2]. Among new energy types including wind, solar, tidal, and biomass power, solar can be considered a key solution for relieving environmental climate change [2]. As a representative technology for solar energy conversion, solar photovoltaic systems (photovoltaics, PV) already demonstrate competitive performance in terms of energy payback time (EPBT), greenhouse gas (GHG), and levelized cost of energy (LCOE) compared with conventional energy sources [3].
Retrofitting distributed rooftops with solar PV is an effective means of promoting “carbon peaking” and “carbon neutral” strategies. Rooftop solar PV is geographically unrestricted. The PV cells can be closely integrated into buildings without taking up additional land resources, not only saving land resources but also improving their utilization rate. In addition, the importance of large-scale PV development has been emphasized in the national renewable energy development plan [4]. Therefore, accurate assessment of long-time-series carbon emission reductions due to rooftop solar PV has become an important research topic.

2. Building Rooftop Recognition from Remote Sensing Images

Previous works on building rooftop extraction and recognition in high-resolution remote sensing images can be divided into two categories: artificial-feature-based methods and deep-learning-based methods [33][5]. The former have mainly utilized geometric, spectral, and background information about building rooftops. Lin and Nevatia [34][6] proposed an edge detection algorithm to extract building rooftops by detecting their walls and shadows. Zhang et al. [35][7] extracted the appearance of the external morphological boundaries of building rooftops based on the geometric linear relationship features after edge detection to improve the accuracy of recognition. Katartzis et al. [36][8] combined edge detection with the Markov model and employed aerial images to extract building rooftops. However, the edge detection method is susceptible to the influence of features similar to building morphology, resulting in building rooftop identification errors. Andres et al. [37][9] developed a threshold determination method based on the K-means algorithm to classify and identify building rooftops in remote sensing images, with a significant reduction in segmentation errors. Liu et al. [38][10] put forward a novel integrated classification method using a Support Vector Machine (SVM) and Decision Tree (DT), combining spectral transform and geographic location information for building rooftop recognition. Most of the methods mentioned above are based on spectral feature difference analysis, which can easily confuse similar features during recognition [39][11]. The latter group are deep-learning models that construct feature space automatically from high-resolution remote sensing images. These methods, especially Fully Convolutional Networks (FCNs) [12], introduce Convolutional Neural Network (CNN) structures to the image semantic domain, and have been widely used in recent years [40,41,42][13][14][15]. Liu et al. [43][16] proposed a new FCN structure consisting of a spatial residual convolution module for extracting building rooftops from remote sensing images, known as spatial residual onset. Shariah et al. [44][17] rewrote FCN without a pooling layer so that the network could retain as much important and useful information from the original image as possible. Ding et al. [45][18] proposed an adversarial method to consider the rectangular shape of buildings while performing building extraction. Although FCN-based methods have shown good performance in building rooftop extraction, such methods are not capable of ulteriorly fusing multi-scale feature information, due to the diverse patterns of buildings rooftops [46][19]. New network structures such as the Global Multiscale Codec Network (GMEDN) [47][20], U-shaped hollow pyramid pooling (USPP) networks [16][21], and Atrous Spatial Pyramid Pooling (ASPP) [48][22] may alleviate this problem in FCNs, but the object boundaries remain blurred and not continuous. The DeepLabv3+ network [49][23] adds the encoder-decoder structure based on an ASPP module to make full use of multi-scale feature information, paving a new direction for building rooftop recognition from high-resolution remote sensing images.

3. The Actual Usable Area of Rooftop Solar PV Systems

The methods for estimating the actual usable area of rooftop solar PV systems can be broadly classified into three categories: fixed-value methods, manual intervention methods, and GIS-based methods [50][24]. The advantage of fixed-value methods is that the calculations are fast. For example, Vardimon [24][25] applied a constant value of 30% to calculate usable rooftop areas in Israel. However, the result was imprecise because the actual factors of influence were not considered. Manual intervention methods make assumptions based on empirical utilization factors [21,51][26][27]. Izquierdo et al. [52][28] estimated the available rooftop solar PV potential in Spain by combining information on population, number of buildings and land use with various coefficients (e.g., shading), although this method lacks generality. GIS-based methods can take more roof details into account, including 3D information from LiDAR, slope, orientation, and building structures [53,54,55][29][30][31]. However, high-resolution remote sensing images (e.g., GF-2, Sentinel 2, etc.) generally discard building elevation information for reasons of economics and data volume [22][32]. Moreover, different local subsidy policies can lead to various practical implementations of rooftop solar PV systems, an aspect which has been neglected in most existing research [56][33]. Table 1 summarizes the different PV policies implemented in different regions in China.
Table 1.
PV subsidy policies in different regions of China.
Region Subsidy Object Subsidy
Beijing Resident-owned industries 0.30 RMB/kWh
Parks and commercial facilities 0.30 RMB/kWh
Universities, primary schools and hospitals 0.40 RMB/kWh
Shanghai Industries and commerce 0.25 RMB/kWh
Schools 0.55 RMB/kWh
Individuals and nursing homes 0.40 RMB/kWh
Yiwu, Zhejiang Residents 0.20 RMB/kWh
Companies providing space 0.30 RMB/kWh
Investment companies 0.10 RMB/kWh
Suzhou, Jiangsu Parks 0.10 RMB/kWh
Finally, different PV installation features (e.g., the installation inclination, the spacing of the arrays, etc.) directly affect the efficiency of the PV panel system. Two popular PV panel mounting installation methods are shown in Figure 1.
Remotesensing 14 03144 g001 550
Figure 1. Different PV installation methods: (a) PV installation on terrace rooftops. It is necessary to consider the installation inclination and the mutual shading between the PV panels. (b) the PV installation on pitched rooftops. The PV panels are installed directly onto the pitched rooftops. Most of the buildings in northern China have terrace rooftops, and most use a combination of arrays to choose the best installation inclination according to the angle of solar radiation.

4. Rooftop Solar PV Potential Assessment

To accurately assess carbon emission reduction due to rooftop solar PVs, a popular large-scale PV electricity generation assessment model, PVWatts, is relatively simple and free to operate [57][34]. PVWatts uses data from the National Solar Radiation Database and can estimate annual and monthly electricity generation based on location, basic parameters, etc. Sampath [58][35] et al. applied this model to estimate rooftop solar PV capacity in Bangalore, India based on remote sensing image segmentation, which has the advantages of being fast and low cost, but the estimation process was carried out under standard experimental conditions. Tang et al. [59][36] employed PVsyst software to simulate the PV electricity generation of different solar cells in Chongqing. Wang et al. [60][37] considered different PV systems and conversion efficiencies in their assessment of PV electricity generation in China. However, most of these studies ignored the effect of weather conditions on the amount of solar radiation, which in turn affects the volume of rooftop solar PV electricity generation. Kais et al. [61][38] proposed a climate-based PV electricity generation assessment model using global meteorological data MERRA to assess the PV potential in the Association of Southeast Asian Nations (ASEAN). The results confirm the geographical correlation between climate and PV electricity generation. However, due to the low resolution of MERRA data, a large amount of valid information was lost, ultimately affecting the accuracy of the evaluation results. In addition, some studies that directly considered climate factors used Global Climate Model (GCM) projections, which do not reliably reflect local climate characteristics [62][39].

References

  1. Koo, C.; Hong, T.; Lee, M.; Park, H.S. Development of a new energy efficiency rating system for existing residential buildings. Energy Policy 2014, 68, 218–231.
  2. Lund, H.; Mathiesen, B.V. Energy system analysis of 100% renewable energy systems—The case of Denmark in years 2030 and 2050. Energy 2009, 34, 524–531.
  3. Bocca, A.; Chiavazzo, E.; Macii, A.; Asinari, P. Solar energy potential assessment: An overview and a fast modeling approach with application to Italy. Renew. Sustain. Energy Rev. 2015, 49, 291–296.
  4. Planning for Renewable Energy in 13th Five-Year; National Development and Reform Commission: Beijing, China, 2016.
  5. Wang, L.; Yan, J.; Mu, L.; Huang, L. Knowledge discovery from remote sensing images: A review. Wiley Interdiscip. Rev. Data Min. Knowl. Discov. 2020, 10, e1371.
  6. Lin, C.; Nevatia, R. Building detection and description from a single intensity image. Comput. Vis. Image Underst. 1998, 72, 101–121.
  7. Zhang, J.; Xiao, W.; Zhang, S.; Huang, S. Device-free localization via an extreme learning machine with parameterized geometrical feature extraction. Sensors 2017, 17, 879.
  8. Katartzis, A.; Sahli, H.; Nyssen, E.; Cornelis, J. Detection of buildings from a single airborne image using a Markov random field model. IGARSS 2001. Scanning the Present and Resolving the Future. In Proceedings of the IEEE 2001 International Geoscience and Remote Sensing Symposium, Sydney, NSW, Australia, 9–13 July 2001; IEEE: New York, NY, USA, 2001; Volume 6, pp. 2832–2834.
  9. Troya-Galvis, A.; Gançarski, P.; Berti-Équille, L. Remote sensing image analysis by aggregation of segmentation-classification collaborative agents. Pattern Recognit. 2018, 73, 259–274.
  10. Liu, C.; Guo, Z.; Fu, N. Applying a new integrated classification method to monitor shifting mangrove wetlands. In Proceedings of the 2010 International Conference on Multimedia Technology, Ningbo, China, 29–31 October 2010; pp. 1–4.
  11. Blaschke, T.; Hay, G.J.; Kelly, M.; Lang, S.; Hofmann, P.; Addink, E.; Feitosa, R.Q.; Van der Meer, F.; Van der Werff, H.; Van Coillie, F.; et al. Geographic object-based image analysis–towards a new paradigm. ISPRS J. Photogramm. Remote Sens. 2014, 87, 180–191.
  12. Long, J.; Shelhamer, E.; Darrell, T. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 7–12 June 2015; pp. 3431–3440.
  13. Rawat, W.; Wang, Z. Deep convolutional neural networks for image classification: A comprehensive review. Neural Comput. 2017, 29, 2352–2449.
  14. Zeiler, M.D.; Fergus, R. Visualizing and understanding convolutional networks. In Proceedings of the European Conference on Computer Vision, Zurich, Switzerland, 6–12 September 2014; Springer: Cham, Switzerland, 2014; pp. 818–833.
  15. Chen, L.C.; Papandreou, G.; Kokkinos, I.; Murphy, K.; Yuille, A.L. Semantic image segmentation with deep convolutional nets and fully connected crfs. arXiv 2014, arXiv:1412.7062.
  16. Liu, P.; Liu, X.; Liu, M.; Shi, Q.; Yang, J.; Xu, X.; Zhang, Y. Building footprint extraction from high-resolution images via spatial residual inception convolutional neural network. Remote Sens. 2019, 11, 830.
  17. Sherrah, J. Fully convolutional networks for dense semantic labelling of high-resolution aerial imagery. arXiv 2016, arXiv:1606.02585.
  18. Ding, L.; Tang, H.; Liu, Y.; Shi, Y.; Zhu, X.X.; Bruzzone, L. Adversarial shape learning for building extraction in VHR remote sensing images. IEEE Trans. Image Process. 2021, 31, 678–690.
  19. Shao, Z.; Tang, P.; Wang, Z.; Saleem, N.; Yam, S.; Sommai, C. BRRNet: A fully convolutional neural network for automatic building extraction from high-resolution remote sensing images. Remote Sens. 2020, 12, 1050.
  20. Ma, J.; Wu, L.; Tang, X.; Liu, F.; Zhang, X.; Jiao, L. Building extraction of aerial images by a global and multi-scale encoder-decoder network. Remote Sens. 2020, 12, 2350.
  21. Liu, Y.; Gross, L.; Li, Z.; Li, X.; Fan, X.; Qi, W. Automatic building extraction on high-resolution remote sensing imagery using deep convolutional encoder-decoder with spatial pyramid pooling. IEEE Access 2019, 7, 128774–128786.
  22. Chen, L.C.; Papandreou, G.; Kokkinos, I.; Murphy, K.; Yuille, A.L. Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE Trans. Pattern Anal. Mach. Intell. 2017, 40, 834–848.
  23. Chen, L.C.; Zhu, Y.; Papandreou, G.; Schroff, F.; Adam, H. Encoder-decoder with atrous separable convolution for semantic image segmentation. In Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 8–14 September 2018; pp. 801–818.
  24. Melius, J.; Margolis, R.; Ong, S. Estimating Rooftop Suitability for PV: A Review of Methods, Patents, and Validation Techniques; NREL: Golden, CO, USA, 2013.
  25. Vardimon, R. Assessment of the potential for distributed photovoltaic electricity production in Israel. Renew. Energy 2011, 36, 591–594.
  26. Ding, H.; Jiang, X.; Shuai, B.; Liu, A.Q.; Wang, G. Context contrasted feature and gated multi-scale aggregation for scene segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 18–23 June 2018; pp. 2393–2402.
  27. Sun, Y.; Wang, S.; Zhang, X.; Chan, T.O.; Wu, W. Estimating local-scale domestic electricity energy consumption using demographic, nighttime light imagery and Twitter data. Energy 2021, 226, 120351.
  28. Izquierdo, S.; Rodrigues, M.; Fueyo, N. A method for estimating the geographical distribution of the available roof surface area for large-scale photovoltaic energy-potential evaluations. Sol. Energy 2008, 82, 929–939.
  29. 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.
  30. Song, X.; Huang, Y.; Zhao, C.; Liu, Y.; Lu, Y.; Chang, Y.; Yang, J. An approach for estimating solar photovoltaic potential based on rooftop retrieval from remote sensing images. Energies 2018, 11, 3172.
  31. Li, Z.; Zhang, Z.; Davey, K. Estimating geographical pv potential using lidar data for buildings in downtown san francisco. Trans. GIS 2015, 19, 930–963.
  32. Zhong, T.; Zhang, Z.; Chen, M.; Zhang, K.; Zhou, Z.; Zhu, R.; Wang, Y.; Lü, G.; Yan, J. A city-scale estimation of rooftop solar photovoltaic potential based on deep learning. Appl. Energy 2021, 298, 117132.
  33. Snape, J.R. Spatial and temporal characteristics of PV adoption in the UK and their implications for the smart grid. Energies 2016, 9, 210.
  34. Psomopoulos, C.S.; Ioannidis, G.C.; Kaminaris, S.D.; Mardikis, K.D.; Katsikas, N.G. A comparative evaluation of photovoltaic electricity production assessment software (PVGIS, PVWatts and RETScreen). Environ. Processes 2015, 2, 175–189.
  35. Sampath, A.; Bijapur, P.; Karanam, A.; Umadevi, V.; Parathodiyil, M. Estimation of rooftop solar energy generation using Satellite Image Segmentation. In Proceedings of the 2019 IEEE 9th International Conference on Advanced Computing (IACC), Tiruchirappalli, India, 13–14 December 2019; pp. 38–44.
  36. Tang, S.; Ding, Y. Dong Mengneng, ye Qiang Potential Analysis of Solar Photovoltaic Power Generation in Chongqing. Chongqing Archit. 2014, 13, 8–11.
  37. Wang, L.Z.; Tan, H.W.; Ji, L.; Wang, D. A method for evaluating photovoltaic potential in China based on GIS platform. IOP Conf. Ser. Earth Environ. Sci. 2017, 93, 012056.
  38. Siala, K.; Stich, J. Estimation of the PV potential in ASEAN with a high spatial and temporal resolution. Renew. Energy 2016, 88, 445–456.
  39. Zhao, X.; Huang, G.; Lu, C.; Zhou, X.; Li, Y. Impacts of climate change on photovoltaic energy potential: A case study of China. Appl. Energy 2020, 280, 115888.
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