Spatial distribution of building energy use in the United States through satellite imagery of the earth at night

被引:20
作者
Fehrer, Derek [1 ]
Krarti, Moncef [1 ]
机构
[1] Univ Colorado, CEAE Dept CB 428, Bldg Syst Program, Boulder, CO 80309 USA
关键词
Building energy use; Nighttime lights; Satellite images; Spatial and temporal energy use maps; LIGHT DATA; CONSUMPTION; CHINA;
D O I
10.1016/j.buildenv.2018.06.033
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Despite the importance of geospatial analysis of energy use in buildings, the data available for such exercises is limited. A potential solution is to use geospatial information, such as that obtained from satellites, to disaggregate building energy use data to a more useful scale. Many researchers have used satellite imagery to estimate the extent of human activities, including building energy use and population distribution. Much of the reported work has been carried out in rapidly developing countries such as India and China where urban development is dynamic and not always easy to measure. In countries with less rapid urbanization, such as the United States, there is still value in using satellite imagery to estimate building energy use for the purposes of identifying energy efficiency opportunities and planning electricity transmission. This study evaluates nighttime light imagery obtained from the VIIRS instrument aboard the SUOMI NPP satellite as a predictor of building energy use intensity within states, counties, and cities in the United States. It is found that nighttime lights can explain upwards of 90% of the variability in energy consumption in the United States, depending on conditions and geospatial scale. The results of this research are used to generate electricity and fuel consumption maps of the United States with a resolution of less than 200 square meters. The methodologies undertaken in this study can be replicated globally to create more opportunities for geospatial energy analysis without the hurdles often associated with disaggregated building energy use data collection.
引用
收藏
页码:252 / 264
页数:13
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