Opportunistic occupancy-count estimation using sensor fusion: A case study

Hobson, Brodie W.; Lowcay, Daniel; Gunay, H. Burak; Ashouri, Araz; Newsham, Guy R.

doi:10.1016/j.buildenv.2019.05.032

Cited by 112 publications

(54 citation statements)

References 36 publications

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“…The 300 W/person assumption for the combined effect of plug loads and lighting is based on (Gunay et al 2016;Bennet and O'Brien 2017;Gunay et al 2017). The assumption to retain 30% of the full-occupancy plug load and lighting electricity use upon occupants' departure is based on several case studies monitoring afterhours electricity use patterns in office buildings (Webber et al 2006;Masoso and Grobler 2010;Brown et al 2012;Hobson et al 2019). While we acknowledge that these deterministic assumptions do not reflect the diversity in occupancy and occupant behaviour patterns, our goal here is merely to understand the effect of variations in the internal heat gains on optimal AHU sequencing decisions.…”

Section: Optimization Scenariosmentioning

confidence: 99%

Deriving sequences of operation for air handling units through building performance optimization

Gunay

Newsham

Ashouri

et al. 2020

Journal of Building Performance Simulation

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Operational parameters of air handling units (AHUs) play an important role in the energy and comfort performance of commercial buildings. Current guidelines to determine these parameters are based on heuristics and are not informed by formal optimization. This paper presents a building performance optimization method to derive sequence of operations for multi-zone AHUs. To this end, 27 variants of a generic EnergyPlus office building model are built representing three levels of occupancy, envelope, and HVAC capacity scenarios. The supply temperature, morning start time, and economizer settings of the model are optimized for the 27 scenarios by using a genetic algorithm. The results highlight that optimal supply temperature setpoints transition from ∼ 12°C to a range of ∼ 16°C to ∼ 20°C over an outdoor temperature range of ∼ 20°C to ∼ 0°C. Energy savings are estimated as ∼ 20% relative to a reference case with a constant supply temperature and preheating/precooling period.

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Section: Optimization Scenariosmentioning

confidence: 99%

Deriving sequences of operation for air handling units through building performance optimization

Gunay

Newsham

Ashouri

et al. 2020

Journal of Building Performance Simulation

Self Cite

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“…Zhu N proposed a two-stage stochastic model to estimate the travel time of the highway corridor [22]. Hobson B W used sensors to estimate occupancy of commercial and institutional buildings [23].…”

Section: Introductionmentioning

confidence: 99%

Evaluation Indexes and Correlation Analysis of Origination–Destination Travel Time of Nanjing Metro Based on Complex Network Method

Wei

Yan

et al. 2020

Sustainability

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The information level of the urban public transport system is constantly improving, which promotes the use of smart cards by passengers. The OD (origination–destination) travel time of passengers reflects the temporal and spatial distribution of passenger flow. It is helpful to improve the flow efficiency of passengers and the sustainable development of the city. It is an urgent problem to select appropriate indexes to evaluate OD travel time and analyze the correlation of these indexes. More than one million OD records are generated by the AFC (Auto Fare Collection) system of Nanjing metro every day. A complex network method is proposed to evaluate and analyze OD travel time. Five working days swiping data of Nanjing metro are selected. Firstly, inappropriate data are filtered through data preprocessing. Then, the OD travel time indexes can be divided into three categories: time index, complex network index, and composite index. Time index includes use time probability, passenger flow between stations, average time between stations, and time variance between stations. The complex network index is based on two models: Space P and ride time, including the minimum number of rides, and the shortest ride time. Composite indicators include inter site flow efficiency and network flow efficiency. Based on the complex network model, this research quantitatively analyzes the Pearson correlation of the indexes of OD travel time. This research can be applied to other public transport modes in combination with big data of public smart cards. This will improve the flow efficiency of passengers and optimize the layout of the subway network and urban space.

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“…The occupancy-based ventilation mode multiplies the ventilation rate with the occupancy profile (see Figure 6). Note that the real life implementation of the occupancy-based ventilation mode requires a sensing technology dedicated for occupancy count estimation (e.g., WiFi-based, camera-based [39][40][41][42]). Determining an appropriate constant ventilation rate in real life requires information on peak occupancy levels.…”

Section: Methodology For Simulation-based Investigationmentioning

confidence: 99%

Sensitivity analysis and optimization of building operations

Gunay

Ouf

Newsham

et al. 2019

Energy and Buildings

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Operator decisions regarding daily and seasonal scheduling of systems' availability and setpoints play an important role in building performance. This paper reviews the key operational parameters of 14 office buildings in Ottawa, Canada. The results revealed that over 60% of the air handling units (AHUs) were not turned off outside of normal occupied hours, and most of them did not have an economizer cycle. The indoor temperature setpoints were between 20 and 24°C. Based on these insights, a building performance simulation (BPS)-based sensitivity analysis was conducted to better understand the energy and comfort performance implications of common operator decisions. Eight operational parameters were studied: AHU start and stop times, seasonal switchover to heating and cooling times, heating and cooling season temperature setpoints, and ventilation rate and mode (i.e., constant or occupancy-based). The results revealed that the AHU start and stop times and the ventilation rate are the most critical operational parameters examined in terms of affecting the energy and comfort performance of the buildings investigated. Subsequently, a mixed-integer genetic algorithm was applied to identify the optimal operational parameters among the set of eight operational parameters investigated for four different heating dominated climate zones, nine different occupancy and three different envelope scenarios. The relationships between the variables of these scenarios and the optimal operational parameters were examined.

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Opportunistic occupancy-count estimation using sensor fusion: A case study

Cited by 112 publications

References 36 publications

Deriving sequences of operation for air handling units through building performance optimization

Deriving sequences of operation for air handling units through building performance optimization

Evaluation Indexes and Correlation Analysis of Origination–Destination Travel Time of Nanjing Metro Based on Complex Network Method

Sensitivity analysis and optimization of building operations

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