A novel population-based occupancy modeling approach for district-scale simulations compared to standard-based methods

Mosteiro-Romero, Martín; Hischier, Illias; Fonseca, Jimeno A.; Schlueter, Arno

doi:10.1016/j.buildenv.2020.107084

Cited by 35 publications

(15 citation statements)

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“…Increasingly, transportation models that predict people's movements in urban areas based on their individual characteristics and activities are used in urban planning. Such microsimulations could serve as a source for occupant and activity modeling in buildings to help improve energy demand predictions in districts [20].…”

Section: Occupantsmentioning

confidence: 99%

“…The uncertainty caused by occupants and climate will continue to have a considerable impact on the results obtained in district-scale energy demand simulation. Previous papers have explored the effects of occupant modeling approaches on the demands predicted by the UBEM City Energy Analyst (CEA) [20,27]. In addition, the effects of the urban heat island on the space cooling demand were analyzed by connecting CEA to microclimate simulation ENVI-met [28,29].…”

Section: Goal and Scope Of This Papermentioning

confidence: 99%

“…The effects of occupancy modeling on the planned systems are explored by comparing the results for three different modeling approaches: deterministic, stochastic, and population-based (PopAp). The models and their effects on the energy demands of the current case study area have been discussed in a previous paper [20], so they are only briefly introduced here.…”

Section: Occupant Modeling Approachesmentioning

confidence: 99%

“…A full analysis of the effects of occupants and microclimate on the area's demands is out of the scope of this paper and can be found in previous publications [20,28]. In this section, these effects are briefly summarized in order to help interpret the results obtained in the supply system sizing and simulation sections.…”

Section: District Energy Demandmentioning

confidence: 99%

“…Selected building properties for each building in the model: conditioned floor area (CFA), main building functions, average envelope U-value (U avg ), window-to-wall ratios, heating and cooling set point and setback temperatures, air exchange rate at 50 Pa (n 50 ), and power density for lighting and appliances (e al ). Adapted from[20].…”

mentioning

confidence: 99%

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Effects of Occupants and Local Air Temperatures as Sources of Stochastic Uncertainty in District Energy System Modeling

Mosteiro-Romero

Schlueter

2021

Energies

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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%.

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Section: Occupantsmentioning

confidence: 99%