With the increasing stock of ageing infrastructure and resource constraints in Singapore, related risks and carbon emissions can be mitigated through long-term resilience planning, automated building inspection, and effective maintenance. Sustainable actions are needed to maintain Singapore's ageing infrastructure. Hence, a state-of-the-art control and management system is required in the form of smart city digital tools. We introduce an Urban Digital Twin (UDT)—GHG App for decision-makers in Singapore's operational building greenhouse gas (GHG) emission mitigation and decarbonisation initiatives. Based on multiple-criteria decision analysis (MCDA), a Potential for Intervention (PFI) map was created to rejuvenate the building system. Decision-makers can use this map to prioritise the rejuvenation of low-carbon building systems in the built environment. A heat map of the PFI results highlights which buildings need urgent rejuvenation based on critical parameters. The GHG App utilises this method to generate maps and enables users to modify parameter weights based on their priorities, automatically updating the map. Users can plan an intervention for buildings with higher PFI values once the map is generated. The GHG App provides interactive data visualisation of 119,872 features representing Singapore's built environment, including the context size of 6,785 existing residential buildings modelled and used to demonstrate the analysis results. Our research findings can contribute to the development of standards for accounting for operational GHG emissions, setting emission limits, and planning decarbonisation in the built environment sector.

In a global context of increasing flexibility in the way workplaces and the districts in which they are located are used, there is a need for occupant-driven approaches to plan urban energy systems. Several authors have suggested the use of agent-based models (ABM) of building occupants in urban building energy simulations due to their ability to reproduce emergent behaviors from individual agents’ actions. However, few works in the literature take full advantage of the ABM paradigm, accounting for both occupant presence and energy-relevant behaviors at the district scale. In this work, we propose a methodology to develop a data-driven, agent-based model of building occupants’ activities and thermal comfort in an urban district. Our methodology combines the use of campus-scale Wi-Fi data to derive feasible occupant activity and location plans, along with thermal preference profiles derived from a longitudinal field study where off-the-shelf, non-intrusive sensors were used to capture the right-here-right-now thermal preference of 35 participants in the same case study district. Our model is then used to explore how different district operation strategies could affect building energy performance in the context of increased workspace flexibility. Our results show that by providing a diversity of building operation conditions, with different buildings having different set point temperatures, occupants’ thermal comfort hours could be improved by an average of about 10% with little effect on district energy performance. Meanwhile, a 6%–15% average decrease in space cooling energy use intensity was observed when implementing occupant-driven ventilation and setpoint controls, regardless of location choice scenario.

Building energy modeling (BEM) is essential for predicting energy use and improving thermal performance in buildings. Traditionally, weather data for BEM comes from built-in tool datasets. Additionally, global atmospheric reanalysis datasets like ERA5, have been used in recent years for BEM. However, the spatial resolution of global atmospheric reanalysis datasets is generally coarse relative to cities, limiting their accuracy in capturing local urban climate effects. Adopting ERA5 as the forcing data, this study examines the use of two urban land surface models, Urban Tethys-Chloris (UT&C) and Urban Weather Generator (UWG), to generate localized weather data for Singapore. The generated local weather data are compared with the data from an on-campus weather station and other weather datasets. Subsequently, these weather datasets are employed as input for an educational building’s energy model that has been validated with energy meter data. The results demonstrate a better agreement between the generated local weather data and locally measured data, compared to the original ERA5 data and typical meteorological year weather data. This leads to an improved accuracy in building energy prediction. By leveraging the global availability of atmospheric reanalysis datasets, this framework for generating local weather data can serve as a universally applicable approach to support building energy design in tropical cities.

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.

The widespread availability of open datasets in urban areas is transforming how urban energy systems are planned, simulated, and visualized. Urban energy models, however, require an understanding of urban dwellers, as their activities create the demands for energy in buildings. In this paper, we explore using campus-scale Wi-Fi data to identify typical occupant activity patterns as an input to an agent-based model of building occupants at the district scale. The data is taken from a Singaporean university's Wi-Fi network at high resolution. Each record comprises a timestamp, a device identifier, the location of the device within the campus, and the access point to which it is connected. The Wi-Fi dataset contains 120 different buildings on campus and 10,300 anonymized individual devices. Activities are then assigned to each location on campus according to the building use type. In order to test the methodology, the activity plans of 27,604 undergraduate students, 8,304 graduate students, and 12,018 employees were simulated over a workweek. The results show the model's ability to produce plausible activity plans but could be improved by implementing sampling rules and expanding the source dataset to include off-peak dates. Nevertheless, using such an agent-based modeling approach at the district scale appears to be a promising methodology to assess the impacts of different planning strategies on occupant behavior and district energy demand.

Smart city initiatives have been a driving force for city-level dataset collection and the development of data-driven applications that benefit effective city management. There is a need to demonstrate use cases for effective city management using the available dataset. Urban Digital Twin (UDT) is a 3D city model that can integrate multi-disciplines and improve systems operability on a digital platform. However, UDTs are developed within organisations, and there is only limited availability of authoritative open 3D datasets to explore the potential of UDT concepts. This paper reports a methodology for creating a UDT platform for visualising and querying city energy data. We demonstrate a bottom-up approach to constructing an integrated 3D city dataset and create a query system for rapid access and navigation of the 3D city dataset through a visualisation platform using Cesium Ion. Various use cases are explored based on the dataset, such as building material stock management, energy demand simulation, electric vehicles (EV) demand and flexibility, and estimation of greenhouse gas (GHG) emissions. These use cases can help decision-makers and stakeholders involved in city planning and management. Furthermore, it provides a guideline for developers willing to create UDT applications for smart city initiatives.

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.

This paper presents a digital twin of a university campus in Singapore as a demonstrator for a digital-twin enabled approach to district energy resilience. This paper focuses mainly on the development of the building energy and occupancy models in the digital twin, which are complemented by a user interface for real-time data visualization and scenario assessment. The building energy demand model of the case study area was calibrated using measured hourly cooling and electricity data collected in the case study area. Occupant presence was estimated using WiFi connection counts, and a simple regression model was developed to assign electricity loads as a function of occupant presence and time of day. The digital twin’s scenario assessment capabilities were explored through scenarios on the long-term effects of climate change and of the increase of remote working and studying as a result of the COVID-19 pandemic. Four different “work-from-home” cases were considered, and three different building operation strategies were assumed for each case. The results show that a decrease in building occupancy post-COVID-19 would lead to minimal space cooling savings in the case study area unless building operation was proactively adjusted to adapt to the new needs of the campus.

District-scale energy demand models can be powerful tools for understanding interactions in complex urban areas and optimising energy systems in new developments. The process of coupling characteristics of urban environments with simulation software to achieve accurate results is nascent. We developed a digital twin through a web map application for a 170ha district-scale university campus as a pilot. The impact on the built environment is simulated with pandemic (COVID-19) and climate change scenarios. The former can be observed through varying occupancy rates and average cooling loads in the buildings during the lockdown period. The digital twin dashboard was built with visualisations of the 3D campus, real-time data from sensors, energy demand simulation results from the City Energy Analyst (CEA) tool, and occupancy rates from WiFi data. The ongoing work focuses on formulating a resilience assessment metric to measure the robustness of buildings to these disruptions. This district-scale digital twin demonstration can help in facilities management and planning applications. The results show that the digital twin approach can support decarbonising initiatives for cities.