02132nas a2200241 4500008004100000022001300041245011800054210006900172260001600241300001400257490000800271520136600279653001501645653001701660653002101677653001701698653001601715653002401731100001201755700001901767700001801786856008601804 2019 eng d a0378778800aAn inverse approach to solving zone air infiltration rate and people count using indoor environmental sensor data0 ainverse approach to solving zone air infiltration rate and peopl cJan-09-2019 a228 - 2420 v1983 a

Physics-based simulation of energy use in buildings is widely used in building design and performance rating, controls design and operations. However, various challenges exist in the modeling process. Model parameters such as people count and air infiltration rate are usually highly uncertain, yet they have significant impacts on the simulation accuracy. With the increasing availability and affordability of sensors and meters in buildings, a large amount of measured data has been collected including indoor environmental parameters, such as room air dry-bulb temperature, humidity ratio, and CO2 concentration levels. Fusing these sensor data with traditional energy modeling poses new opportunities to improve simulation accuracy. This study develops a set of physics-based inverse algorithms which can solve the highly uncertain and hard-to-measure building parameters such as zone-level people count and air infiltration rate. A simulation-based case study is conducted to verify the inverse algorithms implemented in EnergyPlus covering various sensor measurement scenarios and different modeling use cases. The developed inverse models can solve the zone people count and air infiltration at sub-hourly resolution using the measured zone air temperature, humidity and/or CO2 concentration given other easy-to-measure model parameters are known.

10aenergyplus10ainfiltration10aInverse problems10apeople count10asensor data10azone air parameters1 aLi, Han1 aHong, Tianzhen1 aSofos, Marina uhttps://simulationresearch.lbl.gov/publications/inverse-approach-solving-zone-air01978nas a2200145 4500008004100000022001300041245012900054210006900183260001600252300001100268520142500279100001401704700001901718856009501737 2019 eng d a1364032100aLearning occupants’ indoor comfort temperature through a Bayesian inference approach for office buildings in United States0 aLearning occupants indoor comfort temperature through a Bayesian cJan-11-2019 a1095933 a

A carefully chosen indoor comfort temperature as the thermostat set-point is the key to optimizing building energy use and occupants’ comfort and well-being. ASHRAE Standard 55 or ISO Standard 7730 uses the PMV-PPD model or the adaptive comfort model that is based on small-sized or outdated sample data, which raises questions on whether and how ranges of occupant thermal comfort temperature should be revised using more recent larger-sized dataset. In this paper, a Bayesian inference approach has been used to derive new occupant comfort temperature ranges for U.S. office buildings using the ASHRAE Global Thermal Comfort Database. Bayesian inference can express uncertainty and incorporate prior knowledge. The comfort temperatures were found to be higher and less variable at cooling mode than at heating mode, and with significant overlapped variation ranges between the two modes. The comfort operative temperature of occupants varies between 21.9 and 25.4°C for the cooling mode with a median of 23.7°C, and between 20.5 and 24.9°C for the heating mode with a median of 22.7°C. These comfort temperature ranges are similar to the current ASHRAE standard 55 in the heating mode but 2-3°C lower in the cooling mode. The results of this study could be adopted as more realistic thermostat set-points in building design, operation, control optimization, energy performance analysis, and policymaking.

1 aWang, Zhe1 aHong, Tianzhen uhttps://simulationresearch.lbl.gov/publications/learning-occupants%E2%80%99-indoor-comfort01789nas a2200205 4500008004100000245013500041210006900176260001600245300000900261490000700270520100200277653012101279100001701400700001801417700001401435700001301449700001901462700001101481856009101492 2019 eng d00aRevealing Urban Morphology and Outdoor Comfort through Genetic Algorithm-Driven Urban Block Design in Dry and Hot Regions of China0 aRevealing Urban Morphology and Outdoor Comfort through Genetic A cJan-07-2019 a36830 v113 a

In areas with a dry and hot climate, factors such as strong solar radiation, high temperature, low humidity, dazzling light, and dust storms can tremendously reduce people’s thermal comfort. Therefore, researchers are paying more attention to outdoor thermal comfort in urban environments as part of urban design. This study proposed an automatic workflow to optimize urban spatial forms with the aim of improvement of outdoor thermal comfort conditions, characterized by the universal thermal climate index (UTCI). A city with a dry and hot climate—Kashgar, China—is further selected as an actual case study of an urban block and Rhino & Grasshopper is the platform used to conduct simulation and optimization process with the genetic algorithm. Results showed that in summer, the proposed method can reduce the averaged UTCI from 31.17 to 27.43 °C, a decrease of about 3.74 °C, and reduce mean radiation temperature (MRT) from 43.94 to 41.29 °C, a decrease of about 2.65 °C.

10adry and hot areas; outdoor thermal comfort; urban morphology; urban performance simulation; genetic algorithm-driven1 aXu, Xiaodong1 aYin, Chenhuan1 aWang, Wei1 aXu, Ning1 aHong, Tianzhen1 aLi, Qi uhttps://www.mdpi.com/2071-1050/11/13/3683https://www.mdpi.com/2071-1050/11/13/3683/pdf01724nas 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