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This work proposes the use of a data-driven, agent-based model of building occupants’ activities and thermal comfort in an urban university campus in order to assess how district operation strategies can be leveraged to support the transition to flexible work arrangements. The results show that when users are given the flexibility to pursue more comfortable workspaces, they are still comfortable only 58% of the time. BuildSys '23: Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation ISBN:979-8-4007-0230-3

Urban building energy models (UBEM) have the potential to become integral planning tools for district energy systems due to the dynamic, interactive and complex nature of temporal building energy demand patterns. Although the demand patterns are related to the occupancy profiles of buildings supplied by district energy systems, occupant behavior in current UBEM approaches does not usually consider diversity in occupancy profiles among buildings of the same use-type. In this work, a novel method to create context-specific, data-driven commercial building occupancy profiles was used to generate diverse and non-diverse urban building occupant presence models (UBOP). Diverse UBOP randomly assigned occupancy profiles to buildings. Non-diverse UBOP assigned the data-driven mean or median profile to all buildings. ASHRAE standard profiles and occupant densities served as a baseline for comparison. The impact of diverse vs. non-diverse UBOP was assessed by comparing UBEM simulations for district energy efficiency benchmarking, renewable energy integration potential, and district energy system design, using a case study in Singapore. The results demonstrate that, because of the relationship between occupant presence and building systems operation, occupancy profiles are highly sensitive parameters for district energy demand predictions. For the case study, the energy demand estimation is significantly influenced by the shape of occupancy profiles. In particular, the choice of UBOP influences the cooling demand to the degree that district cooling system design decisions might be impacted. Therefore, it is advisable to use diverse UBOP and to run probabilistic UBEM simulations for district energy system design. Applied Energy, 277 ISSN:0306-2619 ISSN:1872-9118

District-scale building energy models can be a powerful tool for the integration of renewable energy sources and efficiency measures in urban areas. One key limitation of these models, however, has been their rather simplified treatment of building occupants. Since it is their activities which create the needs for energy in an area, an improved analysis of the effects of occupants on demand at the district scale is needed. This paper presents a novel population-based approach (PopAp) inspired by agent-based transportation models, in which a population of occupants was defined based on class and employee registers and each was given an individual daily schedule. This approach was then used to assess the effect of occupant presence modeling on district-scale energy demand simulations by comparing the data-centric PopAp method to standard-based deterministic and stochastic approaches. The maximum number of occupants in the area was found to be 33% higher for the deterministic model compared to the data-centric PopAp results, a deviation that was especially pronounced in education buildings. The results for space heating, space cooling and electricity demand for lighting and appliances show that while the mean deviation between models on a yearly basis is within 10% for all demands, on an hourly scale the deviation for space cooling and electricity exceeded 15%. Given the importance of the hourly scale for peak demand prediction for technology sizing, more detailed occupant modeling approaches should be considered when planning energy systems. Building and Environment, 181 ISSN:0360-1323 ISSN:0360-1323

Input uncertainty is one of the major obstacles urban building energy models (UBEM) must tackle. The aim of this paper was to quantify the effects of two of the main sources of stochastic uncertainty, namely building occupants and urban microclimate, on electrical and thermal supply system sizing at the district scale. In order to analyze the effects of the former, three different methods of occupant modeling were implemented in a UBEM. The effects of the urban heat island on system sizing were studied through the use of measured temperature data from a weather station in the case study district compared to measured data from a national weather station. The methods developed were used to assess the sizing and costs of centralized and decentralized technologies for a case study in central Zurich, Switzerland. The choice of occupant modeling approach was found to affect the district’s total annualized costs for space heating and cooling by ±5%, whereas for the costs of electricity the variation was ±8%. Regarding outdoor temperature, the effects on the heating demands proved be negligible, however the costs of the cooling alternatives were found to vary by about 4% at the district scale due to the effect of urban climate, for individual buildings this deviation was as high as 40%.

Standard schedules of occupancy are one of the backbones of building energy simulations. Some schedules that are in use today were published 40 years ago and have not been modified ever since. In this work, we aim at reviewing the representativeness of such standard schedules by comparison to a large data set. We extracted popular times data for commercial buildings, which has the same data structure as occupancy profiles, from the Google maps platform for 13 representative US cities in different climate zones. We use the mean absolute error and the earth mover’s distance as measures of difference in profile scale and shape, respectively. Additionally, we define energy impact metrics, such as the peak value and the time of the peak, to quantify differences that potentially have significant impacts on simulation results. We compared data of restaurant and retail buildings to the respective standard schedules. We found significant differences between standards and data, especially in energy impact metrics. Observed mean peak values were 10 - 40% (occupant capacity) different in the city with the overall best agreement to standards. Moreover, our results indicate that the categorization into weekdays, Saturday and Sunday day types should be reconsidered. In a second step, we compared data among the different cities and found relatively smaller differences, which might be rooted in climatic or socioeconomic influences on peoples’ behavior. This leads us to believe that location-specific data should be considered to more precisely capture occupant behavior.

Urban Building Energy Models are powerful tools for estimating future states of energy consumption and energy generation in buildings. Due to the complexity of these systems, large amounts of data are required, which are often incomplete or unavailable. Through the implementation of building archetypes, models such as the City Energy Analyst minimize the amount of input data. However, these simplifications inherently increase the uncertainty of the expected results. This paper presents a sensitivity analysis of architectural properties (window-to-wall ratio, occupant density and envelope leakiness), thermal properties (U-values, G-values, thermal mass and emissivity of building surfaces), operating parameters (set point temperatures and ventilation rates) and internal loads (heat gains due to occupancy, appliance use and lighting). For this, the study combines a two-step process of sensitivity analysis with Saltelli's extension of the Sobol method and the City Energy Analyst. The methodology is applied to a case study area in central Zurich, Switzerland, comprising 284 buildings with predominantly educational, hospital and residential uses. The results showed that the cooling demand in the area was very strongly influenced by the set point temperature, with other variables having a relatively minor influence. For the heating case a larger number of variables were needed in order to explain variations in demand, primarily the thermal properties of the envelope and air exchange rates of the buildings. This was generally true for all occupancy types, shapes, sizes and locations, showing the importance of accurate estimates of these parameters in urban building energy modeling. On a broader sense, the results contribute to the development of urban energy simulations that are both practical and accurate. CISBAT 2017 International Conference Future Buildings & Districts – Energy Efficiency from Nano to Urban Scale Energy Procedia, 122 ISSN:1876-6102

The air infiltration rate is a highly sensitive variable that influences heating and cooling demand forecasts in urban building energy modeling. This paper analyses the effect of two different simplified modeling techniques of air infiltration - fixed air change rate vs. a model based on wind pressure and air temperatures - on the heating and cooling demand in a district. The urban energy simulation toolbox City Energy Analyst (CEA) is used to simulate a case study in Switzerland, comprising of 24 buildings of various functions. Results indicate that despite the large differences for individual buildings, a fixed infiltration rate model could be sufficient for early design studies of district energy systems, as the impact on the sizing of district energy systems remains relatively low. This comparison will contribute to the continued development of urban energy simulations that are robust, as well as computationally fast. CISBAT 2017 International Conference Future Buildings & Districts – Energy Efficiency from Nano to Urban Scale Energy Procedia, 122 ISSN:1876-6102