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原文信息：
TOM.D: Taking advantage of microclimate data for urban building energy modeling
原文链接：
https://www.sciencedirect.com/science/article/pii/S2666792423000173?via%3Dihub
Highlights
Landsat8的热成像传感器相对于EPW可将CV(RMSE)提高4个点；
CMIP预测可能有助于模拟气候变化对城市的影响；
解决方案考虑了电网的动态电力结构和电价；
极端温度加速能源消耗，遥感可见。
Abstract
Urban Building Energy Modeling (UBEM) provides a framework for decarbonization decision-making on an urban scale. However, existing UBEM systems routinely neglect microclimate effects on building energy consumption, potentially leading to major sources of error. In this work, we attempt to address these sources of error by proposing the large scale collection of remote sensing and climate modeling data to improve the capabilities of existing systems. We explore situations when remote sensing might be most valuable, particularly when high quality weather station data might not be available. We show that lack of access to weather station data is unlikely to be driving existing errors in energy models, as most buildings are likely to be close enough to collect high quality data. We also highlight the significance of Landsat8’s thermal instrumentation to capture pertinent temperatures for the buildings through feature importance visualizations. Our analysis then characterizes the seasonal benefits of microclimate data for energy prediction. Landsat8 is found to provide a potential benefit of an 8% reduction in electricity prediction error in the spring and summertime of New York City. In contrast, NOAA RTMA may provide a benefit of a 2.5% reduction in natural gas prediction error in the winter and spring. Finally, we explore the potential of remote sensing to enhance the quality of energy predictions at a neighborhood level. We show that benefits for individual buildings translates to the regional level, as we can achieve improved predictions for groups of buildings.
Keywords
Urban microclimate
Remote sensing
Machine learning
Land surface temperature
Climate model
Urban heat island
Graphics
Fig. 1. Flowchart outlining the steps taken throughout the process and intermediate data files created and used for subse.
Fig. 2. Median temperature readings for each pixel in New York City over three years, taken by the Landsat8 satellite. Of note, all images were captured from the Landsat instrument between 3:32 PM GMT and 3:41 PM GMT.
Fig. 4. Distance between each building and the closest weather station, measured in meters. The map projection is UTM Zone 18N; thus the gridline differences are measured in meter.
Fig. 7. S3 monthly average MAE relative to EPW. Only the improved prediction months are shown. The seasons listed here are defined for the northern hemisphere.
Fig. 10. A sample of SHAP values computed throughout the Shortwave Infrared 2 Surface Reflectance domain. Note: these are specific to electricity prediction in New York City and relative to a baseline prediction.
Fig. 13. S1 Shap values computed against NOAA RTMA temperature measurements.
关于Applied Energy
本期小编：王桥 审核人：张庭生
《Applied Energy》是世界能源领域著名学术期刊，在全球出版巨头爱思唯尔 (Elsevier) 旗下，1975年创刊，影响因子11.446，CiteScore 20.4，高被引论文ESI全球工程期刊排名第4，谷歌学术全球学术期刊第50，本刊旨在为清洁能源转换技术、能源过程和系统优化、能源效率、智慧能源、环境污染物及温室气体减排、能源与其他学科交叉融合、以及能源可持续发展等领域提供交流分享和合作的平台。开源（Open Access）姊妹新刊《Advances in Applied Energy》现已正式上线。在《Applied Energy》的成功经验基础上，致力于发表应用能源领域顶尖科研成果，并为广大科研人员提供一个快速权威的学术交流和发表平台，欢迎关注！
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