Elastic buildings Calibrated district-scale simulation of occupant-flexible campus operation for hybrid work optimization

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Elastic buildings: Calibrated district-scale simulation of

occupant-ﬂexible campus operation for hybrid work optimization

Mart´ın Mosteiro-Romeroa,b,∗

, Clayton Millerc, Adrian Chongc, Rudi Stouﬀsa

aDepartment of Architecture, College of Design and Engineering, National University of Singapore, 4

Architecture Drive, SDE1 #03-01, Singapore 117566, Singapore

bSingapore-ETH Centre, Future Resilient Systems, CREATE campus, 1 CREATE Way, #06-01 CREATE

Tower, Singapore 138602, Singapore

cDepartment of the Built Environment, College of Design and Engineering, National University of Singapore, 4

Architecture Drive, SDE1 #03-04, Singapore 117566, Singapore

Abstract

Before 2020, the way occupants utilized the built environment had been changing slowly towards

scenarios in which occupants have more choice and ﬂexibility in where and how they work.

The global COVID-19 pandemic accelerated this phenomenon rapidly through lockdowns and

hybrid work arrangements. Many occupants and employers are considering keeping some of these

ﬂexibility-based strategies due to their beneﬁts and cost impacts.

This paper explores how demand-driven control strategies in the built environment might

support the transition to increased workplace ﬂexibility by simulating various scenarios related to

the operational technologies and policies of a real-world campus using a district-scale City Energy

Analyst (CEA) model that is calibrated with measured energy demand data and occupancy

proﬁles extracted from WiFi data. These scenarios demonstrate the energy impact of ramping

building operations up and down more rapidly and eﬀectively to the ﬂex-based work strategies

that may solidify. The scenarios show a 5–15% decrease in space cooling demand due to occupant

absenteeism of 25–75% if centralized building system operation is in place, but as high as 17–

63% if occupancy-driven building controls are implemented. The paper discusses technologies

and strategies that are important in this paradigm shift of operations.

Keywords: demand-driven controls, ﬂexible work arrangements, urban building energy

modeling, data-driven occupancy modeling

∗Corresponding author

Email address: mosteiro@nus.edu.sg (Mart´ın Mosteiro-Romero)

Preprint submitted to Building and Environment April 26, 2023

arXiv:2210.06124v2 [physics.soc-ph] 25 Apr 2023

1. Introduction

Demand-driven control strategies in the built environment have been developed and deployed

for decades. For example, ventilation rates have long been controlled by demand-driven strategies

such as carbon dioxide sensors, from early research projects [1, 2] to practical implementations

since the 1990s [3]. While there is a vast array of potential applications for occupant sensing in

buildings, the vast majority of work on occupant sensing and occupant-centric building controls

focuses on reducing energy waste and improving occupant comfort with regard to lighting and

heating, ventilation and air conditioning (HVAC) [4, 5].

In spite of the huge potential for demand-driven control strategies in buildings, their uptake in

HVAC controls has been limited. This is due to the fact that occupant-related aspects in building

codes are quite simplistic, and modelers tend to use defaults/code assumptions about occupants

to avoid liability, even if they know these values are unrealistic [6]. Hence, ventilation systems

are still generally designed to meet buildings’ peak design capacity, despite consistent evidence

that buildings generally operate far below their design occupancy [7, 8, 9]. While temperature,

airﬂow, and lighting set points for most commercial facilities can be controlled digitally through

the centralized BMS, these set points must still be determined and scheduled manually [10, 11].

Hence, there is still a large potential for energy savings from occupancy-based HVAC and lighting

controls, ranging from 20% to 50% [12].

The problem of building systems being operated by design conditions that do not meet actual

building occupancy is exacerbated by the increasing ﬂexibility in working styles, working hours,

and work arrangements. Flexible work arrangements are centered on the idea that, rather than

bounding people to a ﬁxed desk to carry out their work, they are allowed to choose where they

work, either by selecting a location within their usual workplace or by choosing to work remotely

from home or other locations, such as coﬀee shops or libraries. Workplace strategies of this

kind are often referred to by a number of terms, such as hot-desking, co-working, desk-sharing,

ﬂexible working, oﬃce hoteling, and activity-based workspaces [13]. Flexible working styles allow

employees more freedom to choose their work location, and at the same time desks increasingly

no longer belong to a single employee, but may be shared by diﬀerent occupants [14]. As a result

of this shift in how workplaces are used, ﬂexibility has begun to be an issue not just at the zone

or even building level, but at the district and urban scale, as building users and of their activities

may dynamically change in spatial and temporal distribution.

In early 2020, the COVID-19 pandemic resulted in the restriction of movement of people

worldwide, leading to remote working becoming the norm for all non-essential workers. Knowl-

edge and oﬃce workers quickly transitioned to working from home full time, mainly individuals

from urban areas, with higher paying jobs, beneﬁts, and increased job stability [15]. According

to an October 2020 survey of 10,332 U.S. adults [16], 71% of employed participants were working

from home compared to 20% before the pandemic. This exodus from oﬃce spaces to the home

has shown that such decentralization of oﬃce work is possible and desirable in some situations

[13].

Given that the push for workspace ﬂexibility precedes the COVID-19 pandemic, remote work-

ing arrangements are expected to continue even after the pandemic. There has been a trend to-

wards reducing ﬂoor area per person, and many organizations were already implementing shared

workspaces [17, 18] due to the positive cost-beneﬁts to organizations from a reduced oﬃce foot-

print [19]. 25% of workers in high-income countries are expected to continue remote working

either part-time or full-time after the pandemic [15], and the aforementioned survey of U.S. adults

found that 54% of employees would want to work from home all or most of the time after the

pandemic [16]. Likewise, a survey of 133 U.S. executives and 1200 U.S. oﬃce workers found that

four in ﬁve executives were looking to extend remote work options compared to pre-pandemic

periods, while the majority of employees would prefer to be remote for at least three days per

week and the majority of executives preferred employees to be in person at least three days per

week [20]. Enterprises will therefore need to establish what hybrid work environments, models

of work and new remote work arrangements will look like in the future [15].

The shift to increased remote working will introduce new challenges to the energy and building

sectors. As more and more building occupants adopt ﬂexible working hours, the total scheduled

operating time of HVAC systems in commercial buildings could increase [21]. As workspaces

are operated at less than full occupancy and a share of the employees work from home, there is

a net increase in the operational ﬂoor area per oﬃce worker. A study on a planned mixed-use

neighborhood in Sweden [22] showed that the electricity demand in the district was largest for

scenarios with “soft” conﬁnement, where oﬃce buildings were assumed to be partially-occupied.

However, supporting working from home and teleconferencing can have strongly beneﬁcial

outcomes for emissions if combined with the rationalization and reduction of oﬃce space [23].

There is a need to reconsider how buildings are used and how building systems are operated

to avoid energy waste while supporting the needs of occupants who increasingly require ﬂexible

working spaces and who may only physically attend their workplace on a part-time basis.

1.1. Paper scope and structure

This paper explores how demand-driven control strategies at the district scale might support

the transition to increased workplace ﬂexibility through a data-driven simulation approach. In

particular, we introduce the notion of elastic buildings. Conventional buildings integrate demand

responsiveness into zone-based controls. Elastic buildings utilize a larger array of occupant

demand-driven strategies at the zone, system, building, and possibly even the district scale. In

addition to using occupant sensing technologies, elastic buildings utilize space allocation policies

and technologies that pair people with spaces based on their needs. Thus, in addition to demand-

driven controls to make buildings reactive to variable occupancy and thus reduce energy demand

at the building scale, elastic buildings seek to optimize the use of space in order to reduce energy

demand at the district scale as well.

The advantages of an elastic building system operation are demonstrated by analyzing the

eﬀect of diﬀerent building operation strategies on space cooling demand under diﬀerent working-

from-home (WFH) scenarios. Three building operation strategies are considered. The ﬁrst

represents a traditional, centralized building operation, in which buildings are operated according

to cooling schedules that aﬀect the entire building. The second strategy represents a fully

demand-driven building operation, where buildings are able to provide cooling and ventilation

only to occupied spaces, while unoccupied spaces are maintained at the corresponding setback

temperature. The third strategy represents an elastic building operation, where spaces are

allocated according to demand and buildings are only opened when there is an actual demand

for additional workspaces; buildings are assumed to be operated in a way that prioritizes the

operation of energy-eﬃcient buildings over buildings with higher cooling demands.

These scenarios are tested on a case study of a university campus in Singapore, and the space

cooling demand for each scenario is estimated through a calibrated campus-scale building energy

model that leverages WiFi connection data in order to extract building occupancy patterns at a

campus scale.

The paper is organized as follows. Section 2 gives a general background on demand-driven

control strategies in the built environment, ﬂexible work arrangements, and their eﬀects on

building energy demand. Section 3 describes the development of the campus-scale building

energy demand model, its calibration using building meter and WiFi connection data, as well

as the development of occupancy and electricity demand models based on these data. Section 4

describes the case study area and data collection eﬀort. Subsequently, Section 5 discusses how

these data were used to construct the model according to the aforementioned methodology, as

well as assessing the model’s performance in simulating the baseline case. Section 6 discusses

the simulation results while Section 7 discusses these results’ implications on the design of future

district energy systems and campus operation.

2. Background

2.1. Demand-driven control strategies in the built environment

Central to the eﬀective implementation of occupancy-driven HVAC controls is information

on real-time occupancy and upcoming room occupancy [21]. A number of works in the literature

discuss the various types of sensors available for occupancy detection [24], which sensors are best

suited for each demand-driven application [4], and how data analytics might be implemented in

each case [25]. The most often-discussed occupant sensing technologies are motion sensors such

as PIR sensors, radio frequency identiﬁcation (RFID) technology, vision-based sensors (i.e., cam-

eras), ultrasonic sensors, acoustic sensors, environmental sensors (such as CO2and temperature

sensors), and implicit occupancy sensing through energy meters, etc. [4, 5, 24, 11, 26].

The use of occupancy sensors in commercial lighting control, typically passive infrared (PIR)

motion sensors [27], has become widespread in oﬃce and academic settings [4]. Studies have

shown that the implementation of occupancy sensors could help reduce the electricity demand

for lighting by between 20–60%, depending on the conﬁguration, type of space and type of

occupancy sensor used [14], as well as whether the space was irregularly or regularly occupied [24].

Occupancy-on lighting controls, whereby the lights automatically turned on upon occupancy,

were once seen as a convenience [28], but can lead to energy waste when suﬃcient daylight is

available [29]. Occupants are unlikely to turn oﬀ the lights when automated controls are present,

reducing the energy savings from occupancy sensors by up to 30% [30]. Including illuminance

sensors in addition to occupancy sensors has been found to achieve as much as 65% energy

savings compared to having the lights on all day [31]. Nagy et al. [32] developed an adaptive

control strategy with an illuminance threshold adapted to the preference of the occupants and

a time delay setting (i.e., the time before automatically switching oﬀ the lights if no detectable

occupant movements) adapted to the room’s occupancy pattern, and demonstrated up to 37.9%

electricity savings compared to a standard setting control baseline. Due to their eﬀectiveness,

numerous building codes give credit to motion sensors that control lighting, while several codes

have strict rules against occupancy-on lighting controls [6].

Similarly, a number of occupant detection technologies and HVAC control strategies have

been proposed to reduce energy demand and increase occupants’ thermal comfort. In demand-

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Elasticbuildings:Calibrateddistrict-scalesimulationofoccupant-exiblecampusoperationforhybridworkoptimizationMartnMosteiro-Romeroa,b,,ClaytonMillerc,AdrianChongc,RudiStousaaDepartmentofArchitecture,CollegeofDesignandEngineering,NationalUniversityofSingapore,4ArchitectureDrive,SDE1#03-01,Singapore...

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Elastic buildings Calibrated district-scale simulation of occupant-flexible campus operation for hybrid work optimization

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