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A city-scale roof shape classification using machine learning for solar energy applications

机译：使用机器学习的太阳能应用的城市规模屋顶形状分类

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摘要

Solar energy deployment through PV installations in urban areas depends strongly on the shape, size, and orientation of available roofs. Here we use a machine learning approach, Support Vector Machine (SVM) classification, to classify 10,085 building roofs in relation to their received solar energy in the city of Geneva in Switzerland. The SVM correctly identifies six types of roof shapes in 66% of cases, that is, flat & shed, gable, hip, gambrel & mansard, crossicorner gable & hip, and complex roofs. We classify the roofs based on their useful area for PV installations and potential for receiving solar energy. For most roof shapes, the ratio between useful roof area and building footprint area is close to one, suggesting that footprint is a good measure of useful PV roof area. The main exception is the gable where this ratio is 1.18. The flat and shed roofs have the second highest useful roof area for PV (complex roof being the highest) and the highest PV potential (in GWh). By contrast, hip roof has the lowest PV potential. Solar roof-shape classification provides basic information for designing new buildings, retrofitting interventions on the building roofs, and efficient solar integration on the roofs of buildings. (C) 2017 Elsevier Ltd. All rights reserved.
机译：通过城市地区的光伏装置进行太阳能部署在很大程度上取决于可用屋顶的形状，大小和方向。在这里，我们使用一种机器学习方法，即支持向量机（SVM）分类，相对于瑞士日内瓦市所接收的太阳能，对10085个建筑物屋顶进行分类。 SVM可以在66％的情况下正确识别出六种屋顶形状，即平顶棚，山墙，臀部、,骨和斜s，crossicorner山墙和臀部以及复杂的屋顶。我们根据屋顶对光伏装置的有用面积和接收太阳能的潜力对屋顶进行分类。对于大多数屋顶形状，有用屋顶面积与建筑物占地面积之间的比率接近于1，这表明足迹是衡量有用PV屋顶面积的好方法。主要例外是山墙，该比率为1.18。平屋顶和棚屋屋顶的光伏可用屋顶面积第二高（复杂屋顶最高），光伏潜力最高（以GWh为单位）。相比之下，臀部屋顶的PV电位最低。太阳能屋顶形状分类为设计新建筑物，对建筑物屋顶进行改造干预以及对建筑物屋顶进行有效的太阳能集成提供了基本信息。 （C）2017 Elsevier Ltd.保留所有权利。

著录项

  • 来源
    《Renewable energy》 |2018年第6期|81-93|共13页
  • 作者

    Mohajeri Nahid; Assouline Dan; Guiboud Berenice; Bill Andreas; Gudmundsson Agust; Scartezzini Jean-Louis;

  • 作者单位

    Ecole Polytech Fed Lausanne, Solar Energy & Bldg Phys Lab LESO PB, CH-1015 Lausanne, Switzerland;

    Ecole Polytech Fed Lausanne, Solar Energy & Bldg Phys Lab LESO PB, CH-1015 Lausanne, Switzerland;

    Ecole Polytech Fed Lausanne, Solar Energy & Bldg Phys Lab LESO PB, CH-1015 Lausanne, Switzerland;

    Ecole Polytech Fed Lausanne, Solar Energy & Bldg Phys Lab LESO PB, CH-1015 Lausanne, Switzerland;

    Royal Holloway Univ London, Dept Earth Sci, Egham TW20 0EX, Surrey, England;

    Ecole Polytech Fed Lausanne, Solar Energy & Bldg Phys Lab LESO PB, CH-1015 Lausanne, Switzerland;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Machine learning; Roof shape classification; PV potential; Support Vector Machine;

    机译：机器学习;屋顶形状分类;光伏潜力;支持向量机;

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