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<p>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.<br />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.<br />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.</p>1 aHong, Tianzhen1 aChen, Yixing1 aLuo, Xuan1 aLuo, Na1 aLee, Sang, Hoon  uhttps://simulationresearch.lbl.gov/publications/ten-questions-urban-building-energy02298nas a2200277   4500008004100000022001300041245016000054210006900214260001600283300001600299490000800315520136500323653004401688653002401732653002301756653001801779653002201797653002301819100001201842700001701854700001501871700001901886700001301905700001501918856008701933       2019                        eng d  a0048969700aAssessment of occupant-behavior-based indoor air quality and its impacts on human exposure risk: A case study based on the wildfires in Northern California0 aAssessment of occupantbehaviorbased indoor air quality and its i  cJan-10-2019  a1251 - 12610 v6863 a<p>The recent wildfires in California, U.S., have caused not only significant losses to human life and property, but also serious environmental and health issues. Ambient air pollution from combustion during the fires could increase indoor exposure risks to toxic gases and particles, further exacerbating respiratory conditions. This work aims at addressing existing knowledge gaps in understanding how indoor air quality is affected by outdoor air pollutants during wildfires—by taking into account occupant behaviors (e.g., movement, operation of windows and air-conditioning) which strongly influence building performance and occupant comfort. A novel modeling framework was developed to simulate the indoor exposure risks considering the impact of occupant behaviours by integrating building energy and occupant behaviour modeling with computational fluid dynamics simulation. Occupant behaviors were found to exert significant impacts on indoor air flow patterns and pollutant concentrations, based on which, certain behaviors are recommended during wildfires. Further, the actual respiratory injury level under such outdoor conditions was predicted. The modeling framework and the findings enable a deeper understanding of the actual health impacts of wildfires, as well as informing strategies for mitigating occupant health risk during wildfires</p>10acomputational fluid dynamics siumlation10ahuman exposure risk10aindoor air quality10aNAPA wildfire10aoccupant behavior10arespiratory injury1 aLuo, Na1 aWeng, Wenguo1 aXu, Xiaoyu1 aHong, Tianzhen1 aFu, Ming1 aSun, Kaiyu  uhttps://simulationresearch.lbl.gov/publications/assessment-occupant-behavior-based