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Job Description
We are looking for a full-time Research Associate to work in the Urban Climate Resilience module of the Future Resilient Systems programme, with a focus on Digital Twin-Enabled District Energy System Resilience. The appointment is for at most 12 months. The appointment is expected to start early 2024.
This collaborative research concerns the development of a digital twin for urban climate resilience, especially with respect to building operational GHG emissions, and interactive decision support. As a first pilot study, a digital twin of district energy systems was created for the NUS campus to investigate how past events and possible future scenarios can affect the resilience of these systems. This digital twin comprises a data visualization platform and building energy demand models for scenario assessment. Electricity and cooling meter data were collected for several buildings on campus, as well as WiFi connection logs providing an estimate for the number of occupants in each building. Analyzing the data showed that buildings’ cooling systems are operated in a relatively centralized way, meaning that buildings consume energy for cooling whether they are occupied or not. To explore future scenarios for the case study area, these meter data were used to calibrate building energy demand models for a baseline year. Standards were used for other buildings to make appropriate assumptions for the needs for different building typologies. The platform allows to display these simulation results in a variety of ways, such as by typology, by energy use intensity, or by energy use per capita. Different forecast scenarios have been considered to investigate how different building system operation modes can support the transition to flexible work arrangements post-COVID. The scenarios show there is a significant potential for energy savings by operating buildings in a more occupant-driven way, where only occupied workspaces are actively conditioned.
To expand the platform, additional use cases have been considered, such as modelling the campus’ thermal networks to investigate the effects of different scenarios on the existing cooling infrastructure, or investigating how electric vehicle charging could be used for load management to increase demand flexibility and thus build resilience into the electrical grid. While the actual digital twin platform is being developed as a PhD research, you would contribute to the larger research through one or more building operational energy-related case studies.
Qualifications
• Candidate must have a Master’s degree with at least 2 years’ relevant work experience, and good knowledge, skills and expertise in relevant field or PhD candidates who have at least 2 years’ experience as a fulltime PhD student and expected to complete a PhD degree within six months of starting the appointment may also be considered
• Good time management and planning;
• Excellent command of both spoken and written English;
• Highly motivated individual with capacity to work independently;
• Scripting/computer programming knowledge is required;
• Knowledge of data analytics and/or data visualization is desired;
• Expertise in building simulation and/or the application of deep learning is a plus.
