计算机科学
数据采集
桥(图论)
数据科学
多学科方法
实验数据
数据挖掘
系统工程
工程类
数学
社会科学
医学
统计
操作系统
内科学
社会学
作者
Chao Wang,Martina Ferrando,Francesco Causone,Xing Jin,Xin Zhou,Xing Shi
标识
DOI:10.1016/j.buildenv.2022.109056
摘要
Urban Building Energy Modeling (UBEM) is essential for urban energy-related applications. Its generation mainly requires four data inputs, including geometric data, non-geometric data, weather data, and validation and calibration data. A reliable UBEM depends on the quantity and accuracy of the data inputs. However, the lack of available data and the difficulty in determining stochastic data are two of the main barriers in the development of UBEM. To bridge the research gaps, this paper reviews appropriate acquisition approaches for four data inputs, learning from both building science and other disciplines such as geography, transportation and computer science. In addition, detailed evaluations are also conducted in each part of the study, and the performance of the approaches are discussed, as well as the availability and cost of the implemented data. Systematic discussion, multidisciplinary analysis and comprehensive evaluation are the highlights of this review. • Appropriate and potential data acquisition approaches for UBEM are summarized. • The approaches are learnt from both building science and other disciplines. • Detailed evaluations are conducted on the performance of the approaches. • The availability and cost of the implemented data are also analyzed.
科研通智能强力驱动
Strongly Powered by AbleSci AI