Estimating residential building energy consumption using overhead imagery
Estimating residential building energy consumption using overhead imagery
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DOI：
10.1016/j.apenergy.2020.116018
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发表时间：
2020-12
期刊：
影响因子：
11.2
通讯作者：
Artem Streltsov;Jordan M. Malof;Bohao Huang;Kyle Bradbury
中科院分区：
文献类型：
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作者：
Artem Streltsov;Jordan M. Malof;Bohao Huang;Kyle Bradbury
在低收入和高收入国家，住宅建筑占全球能源消耗的很大比例。在提高运营、经济和环境效率的同时满足建筑能源需求的有效规划需要关于能源消耗的准确、高空间分辨率的信息。这样的信息很难获得，而且大多数估算住宅建筑能耗的模型都需要对个别住宅和社区的详细了解，而这些知识不太可能大规模获得。为了满足这一需求，我们介绍了一种从高空图像(如卫星、天线)自动估计单个建筑能耗的方法，并演示了空间聚集的效果，以进一步提高精度。我们使用三步估计过程：(1)使用卷积神经网络自动分割高空图像中的建筑物，并根据类型(住宅或商业)对其进行分类；(2)从识别出的住宅建筑中提取特征(如面积、周长、建筑密度)；以及(3)使用随机森林回归从这些特征中估计建筑能耗。该方法的预测能力在两个地点进行了评估：佛罗里达州的盖恩斯维尔和加利福尼亚州的圣地亚哥。建筑探测器分别正确识别了盖恩斯维尔和圣地亚哥84%和88%的建筑。住宅建筑的分类成功率为99%，商业建筑的分类成功率为74%。在确定了住宅建筑后，该方法预测了盖恩斯维尔和圣地亚哥的个人建筑级能耗，R2分别为0.28和0.38。将盖恩斯维尔200×200米和1000×1000米的小社区的能源消耗估计聚合在一起，得到的R2分别为0.91和0.97。我们还探讨了圣地亚哥和盖恩斯维尔的估计对训练数据及其大小的敏感性。我们的结果表明，使用俯视图像来估计建筑物的大小在估计住宅建筑能耗方面比普通替代方法具有更高的预测能力。
Residential buildings account for a large proportion of global energy consumption in both low-and high-income countries. Efficient planning to meet building energy needs while increasing operational, economic, and environmental efficiency requires accurate, high spatial resolution information on energy consumption. Such information is difficult to acquire and most models for estimating residential building energy consumption require detailed knowledge of individual homes and communities which are unlikely to be available at a large scale. To address this need, we introduce a methodology for automatically estimating individual building energy consumption from overhead imagery (eg satellite, aerial) and demonstrate the effect of spatial aggregation for further improving accuracy. We use a three-step estimation process by which we (1) automatically segment buildings in overhead imagery using a convolutional neural network and classify them by type (residential or commercial),(2) extract features (eg area, perimeter, building density) from those identified residential buildings, and (3) use random forests regression to estimate building energy consumption from those features. The predictive capability of this approach is evaluated in two locations: Gainesville, Florida, and San Diego, California. The building detector correctly identifies 84% and 88% of buildings in Gainesville and San Diego, respectively. The type of building is classified successfully 99% of the time for residential buildings and 74% of the time for commercial buildings. With residential buildings identified, this approach predicted individual building-level energy consumption with an R 2 of 0.28 and 0.38 for Gainesville and San Diego, respectively. Aggregating the energy consumption estimates across small neighborhoods of size 200× 200 m and 1000× 1000 m in Gainesville results in an R 2 of 0.91 and 0.97, respectively. We also explore the sensitivity of estimates in San Diego and Gainesville to the training data and its size. Our results suggest that using overhead imagery to estimate the size of buildings has a higher predictive power in estimating residential building energy consumption than common alternatives.