02294nas a2200193 4500008004100000022001300041245005200054210005200106260001600158300001100174490000800185520173700193100001901930700001701949700001401966700001201980700002001992856008802012 2020 eng d a0360132300aTen questions on urban building energy modeling0 aTen questions on urban building energy modeling cJan-01-2020 a1065080 v1683 a

Buildings in cities consume up to 70% of all primary energy. To achieve cities’ energy and climate goals, it is necessary to reduce energy use and associated greenhouse gas emissions in buildings through energy conservation and efficiency improvements. Computational tools empowered with rich urban datasets can model performance of buildings at the urban scale to provide quantitative insights for stakeholders and inform their decision making on urban energy planning, as well as building energy retrofits at scale, to achieve efficiency, sustainability, and resilience of urban buildings.
Designing and operating urban buildings as a group (from a city block to a district to an entire city) rather than as single individuals requires simulation and optimization to account for interactions among buildings and between buildings and their surrounding urban environment, and for district energy systems serving multiple buildings with diverse thermal loads across space and time. When hundreds or more buildings are involved in typical urban building energy modeling (UBEM) to estimate annual energy demand, evaluate design or retrofit options, and quantify impacts of extreme weather events or climate change, it is crucial to integrate urban datasets and UBEM tools in a seamless automatic workflow with cloud or high-performance computing for users including urban planners, designers and researchers.
This paper presents ten questions that highlight significant UBEM research and applications. The proposed answers aim to stimulate discussion and provide insights into the current and future research on UBEM, and more importantly, to inspire new and important questions from young researchers in the field.

1 aHong, Tianzhen1 aChen, Yixing1 aLuo, Xuan1 aLuo, Na1 aLee, Sang, Hoon uhttps://simulationresearch.lbl.gov/publications/ten-questions-urban-building-energy01724nas a2200229 4500008004100000022001300041245006000054210006000114260001600174300001100190490000800201520104600209653002201255653001501277653001701292653002601309653001801335653001601353100002001369700001901389856008601408 2019 eng d a0360132300aValidation of an inverse model of zone air heat balance0 aValidation of an inverse model of zone air heat balance cJan-08-2019 a1062320 v1613 a

This paper presents the validation method and results of an inverse model of zone air heat balance. The inverse model, implemented in EnergyPlus and published in a previous article [1], calculates highly uncertain model parameters such as internal thermal mass and infiltration airflow by inversely solving the zone air heat balance equation using the easy-to-measure zone air temperature data. The paper provides technical details of validation from the experiments using LBNL’s Facility for Low Energy eXperiment in Buildings (FLEXLAB) that measures zone air temperature under the controlled experiment of two levels of internal mass and four levels of infiltration airflow. The simulation results of the zone infiltration airflow and internal thermal mass from the inverse model agree well with the measured data from the FLEXLAB experiments. The validated inverse model in EnergyPlus can be used to enhance the energy modeling of existing buildings that enables energy performance assessments for energy efficiency improvements.

10aEnergy simulation10aenergyplus10ainfiltration10ainternal thermal mass10ainverse model10asensor data1 aLee, Sang, Hoon1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/validation-inverse-model-zone-air