The Remote Sensing of Urban Energy Efficiency

城市能源效率遥感世界每年浪费的能源多于消耗的能源。据估计，现有建筑消耗了世界上大约40%的能源，其中50%被浪费了，同时产生了三分之一的地球温室气体（ghg）。虽然世界各地都有节能项目，但大多数项目只衡量能源消耗，而不衡量能源浪费。这是因为废物能源是肉眼看不见的，而且很难测量、监控或管理我们看不见的东西。然而，废物能源约占城市能源需求的50%，但在城市能源效率方程中却不存在。为了解决这一挑战，我的研究计划的长期目标是评估加拿大制造的TABI-1800（热机载宽带成像仪）获得的高分辨率机载热红外（TIR）图像是否可以用于量化，地图和监测城市能源效率随着时间的推移，以单个房屋的分辨率，以及拥有超过100万居民的加拿大大城市的规模。我们建议可以通过实现3个短期目标来应对这一挑战：(i)了解环境和传感器相关噪声对原始热图像的影响，并开发和测试方法来规范其影响。（ii）在非常大和详细的热场景中评估基于地理对象的图像分析和机器学习方法的组合，以更好地将发射的TIR建筑信号从背景场景中隔离出来，从而改进针对建筑物的热损失图和测量，而不是混合景观特征。（iii）通过将这些改进的热损失测量与建筑消耗数据和机器学习相结合，发展最先进的城市热红外遥感技术，直接从整个城市中心的机载TIR数据开发第一批城市能源效率测量方法。通过使整个城市的能源效率以单个房屋的分辨率可见，我们设想了居民和社区节省资金，增加家庭舒适度，减少温室气体，支持绿色经济发展（修复低效房屋），验证建筑质量和减少昂贵能源浪费的潜力。这也将创造新的机会，促进加拿大在城市能源效率方面的领导地位，并向全世界出口加拿大TIR科技解决方案。

THE REMOTE SENSING OF URBAN ENERGY EFFICIENCY The world wastes more energy than it uses - every year. It is estimated that the existing building stock consumes approximately 40% of the world's energy, of which 50% is wasted, while contributing to one third of planetary greenhouse gases (GHGs). Although energy-efficiency programs exist world-wide, most only measure energy consumption - not waste energy. This is because waste-energy is invisible to the human eye, and it's very difficult to measure, monitor, or manage something that we can't see. Yet, waste-energy is responsible for an estimated 50% of urban energy demand, but is missing from urban energy efficiency equations. In order to solve this challenge, the long-term goal of my research program is to evaluate whether high-resolution airborne thermal infrared (TIR) imagery acquired by the Canadian made TABI-1800 (Thermal Airborne Broadband Imager) can be used to quantify, map and monitor urban energy efficiency over time, at the resolution of individual houses, and the scale of a large Canadian city with over 1 million residents. We propose that this challenge can be met by achieving 3 short term goals: (i) To understand the effects that environmental and sensor related noise have on the raw thermal image and to develop and test methods to normalize their effects. (ii) To evaluate a combination of Geographic Object-Based Image Analysis and machine learning methods over very large and detailed thermal scenes, to better isolate the emitted TIR building signal from the background scene, resulting in improved heat-loss maps and measures specific to the building, rather than a mix of landscape features. (iii) To evolve the state-of-the-art in urban thermal infrared remote sensing by combining these improved heat-loss measures with building consumption data and machine learning to develop the first measures of urban energy efficiency directly from airborne TIR data for entire urban centres. By making energy efficiency visible for entire cities at the resolution of a single house, we envision the potential for residents and communities to save money, increase home comfort, reduce GHGs, support the development of a green economy (to fix inefficient homes), verify building construction quality and reduce the waste of expensive energy resources. This will also create new opportunities to promote Canadian leadership in urban energy efficiency and export Canadian TIR science and technology solutions world-wide.