Building Monitoring by Remote Sensing and Analysis of Distributed Photovoltaic Construction Potentials

Guanghui Wang; Xinming Tang; Tao Zhang; Hailun Dai; Yaoyao Peng

doi:10.15302/J-SSCAE-2021.06.017

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Strategic Study of CAE ›› 2021, Vol. 23 ›› Issue (6) : 92-100. DOI: 10.15302/J-SSCAE-2021.06.017

Research on the Strategies and Paths for Carbon Peak and Carbon Neutrality in China

Orginal Article

Building Monitoring by Remote Sensing and Analysis of Distributed Photovoltaic Construction Potentials

Guanghui Wang¹^,² ,
Xinming Tang¹ ,
Tao Zhang¹ ,
Hailun Dai¹^,³ ,
Yaoyao Peng¹^,³

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Abstract

Distributed photovoltaic is a typical type of clean energy and has the characteristics of small investment and fast construction. Distributed photovoltaic power can address the power consumption problem in rural areas with energy shortage and industrial areas with high load densities. Building roofs are important carriers for distributed photovoltaic facilities and thus directly related to the construction potential of distributed photovoltaic. Therefore, monitoring and analyzing the spatial distribution of buildings in China has an important reference value for the planning and construction of distributed photovoltaic. In this research, we use 2 m-resolution satellite remote-sensing images as the data source and extract the building zones in China using the deep learning technology. The areas of building roofs in typical regions are calculated according to the proportion coefficients of these regions. Subsequently, we analyze the spatial characteristics of the building roofs in China and the distribution pattern for construction potentials of distributed photovoltaic. Based on this, we propose some suggestions for the construction path of distributed photovoltaic in different areas considering the spatial distribution of population. The research shows that the accuracy of building extraction by remote sensing was 81.63%, which can satisfy subsequent data analysis requirement. Approximately 1.4×10⁴ km² of building roofs in China have the potential to develop distributed photovoltaic. We suggest that distributed photovoltaic should be developed hierarchically following the principle of local power generation and local consumption. The provinces in China can be categorized into four echelons and the construction should start from the east part of China that is densely-populated and has great potentials for distributed photovoltaic development. Moreover, it is necessary to establish a nation-wide dynamic monitoring mechanism for distributed photovoltaic construction based on satellite remote sensing, thereby supporting the dynamic update of the distributed photovoltaic planning path.

Keywords

satellite remote sensing / building extraction / distributed photovoltaic / deep learning

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Guanghui Wang, Xinming Tang, Tao Zhang, Hailun Dai, Yaoyao Peng. Building Monitoring by Remote Sensing and Analysis of Distributed Photovoltaic Construction Potentials. Strategic Study of CAE, 2021, 23(6): 92‒100 https://doi.org/10.15302/J-SSCAE-2021.06.017

References

Publishing order | Descend order by publishing year | Descend order by cited within

[1]	魏文栋. 能源革命：实现碳达峰和碳中和的必由之路 [J]. 探索与争鸣, 2021 (9): 23–25. Wei W D. Energy revolution: The only way to achieve emission peak and carbon neutrality [J]. Exploration and Free Views, 2021 (9): 23–25.

[2]	林卫斌, 吴嘉仪. 碳中和愿景下中国能源转型的三大趋势 [J]. 价格理论与实践, 2021 (7): 21–23. Lin W B, Wu J Y. Three trends for China’s energy transition under the carbon neutrality vision [J]. Price: Theory & Practice, 2021 (7): 21–23.

[3]	李十中. 推动新能源革命促进实现碳中和目标 [J]. 学术前沿, 2021 (14): 42–51. Li S Z. Promoting the new energy revolution and achieving the goal of carbon neutrality [J]. Frontiers, 2021 (14): 42–51.

[4]	黄震, 谢晓敏. 碳中和愿景下的能源变革 [J]. 中国科学院院刊, 2021, 36(9): 1010–1018. Huang Z, Xie X M. Energy revolution under vision of carbon neutrality [J]. Bulletin of Chinese Academy of Sciences, 2021, 36(9): 1010–1018.

[5]	高林, 郑雅文, 杨东泰, 等. 构建碳中和电力系统——碳中和公式 [J]. 科学通报, 2021, 66(31): 1–5. Gao L, Zheng Y W, Yang D T, et al. Criterial equation of carbon neutrality for power systems [J]. Chinese Science Bulletin, 2021, 66(31): 1–5.

[6]	张鸿宇, 黄晓丹, 张达, 等. 加速能源转型的经济社会效益评估 [J]. 中国科学院院刊, 2021, 36(9): 1039–1048. Zhang H Y, Huang X D, Zhang D, et al. Evaluating economic and social benefits of accelerated energy transition [J]. Bulletin of Chinese Academy of Sciences, 2021, 36(9): 1039–1048.

[7]	徐伟, 王雪, 孙维娜, 等. 太阳能建筑能效研究国外综述 [J]. 智能建筑与智慧城市, 2020 (11): 43–44. Xu W, Wang X, Sun W N, et al. Review on energy efficiency of solar energy buildings abroad [J]. Intelligent Building & Smart City, 2020 (11): 43–44.

[8]	张乾, 辛晓洲, 张海龙, 等. 基于遥感数据和多因子评价的中国地区建设光伏电站的适宜性分析 [J]. 地球信息科学学报, 2018, 20(1): 119–127. Zhang Q, Xin X Z, Zhang H L, et al. Suitability analysis of photovoltaic power plants in China using remote sensing data and multi-criteria evaluation [J]. Journal of Geo-information Science, 2018, 20(1): 119–127.

[9]	王钧, 陈明媛, 冯冠华, 等. 太阳能光伏系统建筑应用技术适宜性探研与典例分析 [J]. 建筑节能, 2021, 49(8): 91–96. Wang J, Chen M Y, Feng G H, et al. Technical applicability of solar photovoltaic system used in building and typical case analysis [J]. Building Energy Efficiency, 2021, 49(8): 91–96.

[10]	姚春妮, 马欣伯, 罗多. 碳达峰目标下太阳能光电建筑应用发展规模预测研究 [J]. 建设科技, 2021 (11): 33–35. Yao C N, Ma X B, Luo D. Prediction research on mid- and longterm development goals of solar photovoltaic application in buildings under carbon peak target [J]. Construction Science and Technology, 2021 (11): 33–35.

[11]	苏翠霞, 邢艳艳, 林奕, 等. 我国建筑屋顶光伏应用潜力分析 [J]. 浙江建筑, 2021, 38(3): 59–60. Su C X, Xing Y Y, Lin Y, et al. Analysis on photovoltaic application potential of building roofing in China [J]. Zhejiang Construction, 2021, 38(3): 59–60.

[12]	王思琪, 李铮伟, 王海, 等. 新供给模式下城市建筑屋顶光伏发电的利用潜力分析 [J]. 太阳能, 2021 (6): 11–17. Wang S Q, Li Z W, Wang H, et al. Analysis on PV power generation utilization potential of urban building rooftop under new supply mode [J]. Solar Energy, 2021 (6): 11–17.

[13]	朱俊杰, 范湘涛, 邵芸. 高分辨率SAR与光学图像融合用于建筑物屋顶提取 [J]. 中国科学院研究生院学报, 2006 (2): 178–185. Zhu J J, Fan X T, Shao Y. Building’s roof extraction by fusing high-resolution SAR with optical images [J]. Journal of University of Chinese Academy of Sciences, 2006 (2): 178–185.

[14]	谭衢霖. 高分辨率多光谱影像城区建筑物提取研究 [J]. 测绘学报, 2010, 39(6): 618–623. Tan Q L. Urban building extraction from VHR multi-spectral images using object-based classification [J]. Acta Geodaetica et Cartographica Sinica, 2010, 39(6): 618–623.

[15]	李亮, 王成, 李世华, 等. 基于机载LiDAR数据的建筑屋顶点云提取方法 [J]. 中国科学院大学学报, 2016, 33(4): 537–541. Li L, Wang C, Li S H, et al. Building roof point extraction based on airborne LiDAR data [J]. Journal of University of Chinese Academy of Sciences, 2016, 33(4): 537–541.

[16]	赵传, 张保明, 陈小卫, 等. 一种利用点云邻域信息的建筑物屋顶面高精度自动提取方法 [J]. 测绘学报, 2017, 46(9): 1123– 1134. Zhao C, Zhang B M, Chen X W, et al. Accurate and automatic building roof extraction using neighborhood information of point clouds [J]. Acta Geodaeticaet Cartographica Sinica, 2017, 46(9): 1123–1134.

[17]	徐佳伟, 刘伟, 单浩宇, 等. 基于PRCUnet的高分遥感影像建筑物提取 [J]. 地球信息科学学报, 2021, 23(10): 1838–1849. Xu J W, Liu W, Shan H Y, et al. High-resolution remote sensing image building extraction based on PRCUnet [J]. Journal of Geoinformation Science, 2021, 23(10): 1838–1849.

[18]

卢彻, 徐胜华, 朱军. 改进U-Net的高分影像建筑物提取方法 [EB/OL]. (2021-09-23)[2021-10-15]. http://kns.cnki.net/kcms/ detail/11.4415.P.20210922.1137.002.html. Lu C, Xu S H, Zhu J. Building extraction from high resolution remote sensing image based on improved U-Net model [EB/ OL]. (2021-09-23)[2021-10-15]. http://kns.cnki.net/kcms/ detail/11.4415.P.20210922.1137.002.html.

[19]	刘德祥, 张海荣, 承达瑜, 等. 融合注意力机制的建筑物提取方法 [J]. 遥感信息, 2021, 36(4): 119–124. Liu D X, Zhang H R, Cheng D Y, et al. A Building extraction method combined attention mechanism [J]. Remote Sensing Information, 2021, 36(4): 119–124.

[20]	吴开顺, 郑道远, 陈妍伶, 等. 中国典型城市建筑物实例数据集 [J]. 中国科学数据(中英文网络版), 2021, 6(1): 182–190. Wu K S, Zheng D Y, Chen Y L, et al. A dataset of building instances of typical cities in China [J]. China Scientific Data, 2021, 6(1): 182–190.