**6. Conclusions**

Urban-scale building energy consumption data are important for city managers or urban planners. However, an open source national building energy consumption database is not available in China. Instead of an energy consumption survey or measurement, urban scale building energy simulation can play an essential role in sustainable development during the urbanization process. It can enable high resolution analysis to estimate city level energy and track dynamic change. The requirement for citywide dynamic energy consumption information is urgen<sup>t</sup> for city planning and energy policy making. Urban planners and policy makers can use the urban energy simulation platform to support urban-scale spatial and temporal decision-making on energy.

To develop such an urban-scale building energy platform, this paper demonstrates our work on generating a representative building energy consumption database for typical residential building, small o ffice building, and large o ffice building. The reference residential building, small and large o ffice building energy models for Wuhan China were developed in EnergyPlus. The baseline residential reference building was calibrated using China's CRECS2012 building energy survey data to consider di fferent building characteristics and occupants' unique HVAC usage patterns. Stochastic simulations were conducted to generate the numerical building energy consumption database. Three di fferent construction levels were considered to reflect building vintages. Energy consumption distributions were adjusted using Wuhan's housing price and rent data.

Urban-scale building energy simulation requires engineering knowledge and computational resources, which creates a barrier for fast decision-making support. To solve this challenge, the building energy consumption database was further used to develop statistical regression models. To better illustrate our methods and make it easy and friendly to use, we developed a building simulation platform based on JavaEE technologies and standard WebServices. The platform and APIs are expected to provide design support for new constructions as well as for building retrofit. Combined with GIS database, the API can be easily used to develop a 3D urban energy prediction platform. With the support of data visualization, city managers and urban planners can check the spatial and temporal building energy distributions in a city area and assemble fast polices regarding building e fficiency and sustainability.

**Author Contributions:** Conceptualization, C.D. and N.Z.; methodology, C.D.; software, C.D.; validation, C.D.; formal analysis, C.D.; investigation, C.D.; resources, C.D. and N.Z.; data curation, C.D.; writing—original draft preparation, C.D.; writing—review and editing, C.D. and N.Z.; visualization, C.D.; supervision, N.Z.; project administration, N.Z.; funding acquisition, N.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the United States Department of Energy under Contract No. DE-AC02-05CH11231 and Energy Foundation China.

**Acknowledgments:** The authors would like to thank Chinese researchers in many research institutes by providing materials, guidance, and advice.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
