Data acquisition for urban building energy modeling: A review

• Published in: Building and environment Vol. 217; p. 109056
• Main Authors: Wang, Chao, Ferrando, Martina, Causone, Francesco, Jin, Xing, Zhou, Xin, Shi, Xing
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.06.2022; Elsevier BV
• Subjects: Cost analysis; Data acquisition; Data science; Energy; Geography; Meteorological data; Modelling; UBEM; Urban building energy modeling; Urban energy simulation; Data science; UBEM; Urban building energy modeling; Urban energy simulation; Data acquisition
• ISSN: 0360-1323; 1873-684X
• DOI: 10.1016/j.buildenv.2022.109056

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.