Digital Futures category
The University of Technology Sydney’s Energy Intelligence project uses advanced data analytics and AI-driven insights to optimise building performance and reduce campus emissions. Launched in May 2024 as part of UTS’s Building Optimisation Strategy, the program draws on existing data sources, including half-hourly electricity metering, WiFi-derived occupancy data, HVAC telemetry, building management systems and weather data.
Led by the Data, Analytics and Insights Unit in partnership with Facilities and Operations, the initiative applies machine-learning forecasting, anomaly detection and signal processing to identify where, when and why energy is used. These insights have directly informed decisions on lighting controls, building closures, cleaning schedules and demand management. By using existing data rather than capital-intensive retrofits, UTS demonstrates how digital insight can drive measurable sustainability gains and support a smarter, lower-carbon campus.
Impacts & Benefits
- Delivered around 6,000 tCO₂e of abatement, primarily through reduced out-of-hours energy use and smarter demand management.
- Generated approximately $2.5 million in savings across the Building Optimisation Strategy since May 2024.
- Achieved further operational savings, with cleaning optimisation contributing around $200,000 per year.
- Used existing data for sustainability insight, drawing on metering, WiFi occupancy, HVAC, building management and weather data without major new hardware.
- Improved campus operations, aligning lighting, access and cleaning schedules more closely with real occupancy and building use.
Leadership & Engagement
- Made analytics central to sustainability operations, turning raw campus data into practical, decision-ready insights.
- Built strong collaboration between data and operations teams, with the Data, Analytics and Insights Unit producing insights and Facilities and Operations implementing changes.
- Shifted energy management from periodic review to predictive optimisation, with a demand-management capability in development to forecast peaks and trigger pre-emptive action.
- Demonstrated a no-capital digital sustainability model, achieving measurable outcomes through data, decisions and operational ownership rather than major retrofits.
- Aligned digital innovation with institutional climate commitments, supporting UTS’s Race to Zero, Climate Emergency Declaration and Digital UTS Strategy.
Wider Societal Impact
- Created a replicable model for data-led decarbonisation, relevant to universities and other large organisations with metered electricity and building management systems.
- Supported grid stability, by flattening and shifting peak demand to reduce pressure on the electricity network.
- Demonstrated low-capital climate action, showing how organisations can use existing data to identify savings and emissions reductions.
- Built future workforce capability, developing applied skills in data science, energy analytics, machine learning, signal processing and operational decision-making.
- Modelled data-driven environmental stewardship, showing students, staff and the wider community how digital systems can support practical sustainability outcomes.
Top 3 learnings
Supported by
Category finalists
Digital Futures
Digital Futures
Digital Futures category
The University of Technology Sydney’s Energy Intelligence project uses advanced data analytics and AI-driven insights to optimise building performance and reduce campus emissions. Launched in May 2024 as part of UTS’s Building Optimisation Strategy, the program draws on existing data sources, including half-hourly electricity metering, WiFi-derived occupancy data, HVAC telemetry, building management systems and weather data.
Led by the Data, Analytics and Insights Unit in partnership with Facilities and Operations, the initiative applies machine-learning forecasting, anomaly detection and signal processing to identify where, when and why energy is used. These insights have directly informed decisions on lighting controls, building closures, cleaning schedules and demand management. By using existing data rather than capital-intensive retrofits, UTS demonstrates how digital insight can drive measurable sustainability gains and support a smarter, lower-carbon campus.
Top 3 learnings
Impacts & Benefits
- Delivered around 6,000 tCO₂e of abatement, primarily through reduced out-of-hours energy use and smarter demand management.
- Generated approximately $2.5 million in savings across the Building Optimisation Strategy since May 2024.
- Achieved further operational savings, with cleaning optimisation contributing around $200,000 per year.
- Used existing data for sustainability insight, drawing on metering, WiFi occupancy, HVAC, building management and weather data without major new hardware.
- Improved campus operations, aligning lighting, access and cleaning schedules more closely with real occupancy and building use.
Leadership & Engagement
- Made analytics central to sustainability operations, turning raw campus data into practical, decision-ready insights.
- Built strong collaboration between data and operations teams, with the Data, Analytics and Insights Unit producing insights and Facilities and Operations implementing changes.
- Shifted energy management from periodic review to predictive optimisation, with a demand-management capability in development to forecast peaks and trigger pre-emptive action.
- Demonstrated a no-capital digital sustainability model, achieving measurable outcomes through data, decisions and operational ownership rather than major retrofits.
- Aligned digital innovation with institutional climate commitments, supporting UTS’s Race to Zero, Climate Emergency Declaration and Digital UTS Strategy.
Wider Societal Impact
- Created a replicable model for data-led decarbonisation, relevant to universities and other large organisations with metered electricity and building management systems.
- Supported grid stability, by flattening and shifting peak demand to reduce pressure on the electricity network.
- Demonstrated low-capital climate action, showing how organisations can use existing data to identify savings and emissions reductions.
- Built future workforce capability, developing applied skills in data science, energy analytics, machine learning, signal processing and operational decision-making.
- Modelled data-driven environmental stewardship, showing students, staff and the wider community how digital systems can support practical sustainability outcomes.
Supported by
Category finalists
Digital Futures
Digital Futures
