Buildings 2.0 – How data drives energy and water efficiency

Effective data management and the strategic use of technology deliver significant energy and water efficiency in buildings. From design and construction to the operational phase, digital technologies simplify works while increasing affordability, health, and comfort for building occupants.

Digital energy and water efficiency technologies and software are mature and readily available.

Smart renovations are most successful when they start with a clear roadmap—assessing both the physical condition of the building envelope and the digital readiness of systems—and proceed in parallel across both domains. This dual approach reduces total renovation costs, prevents technology lock-ins, and maximises buildings’ potential to act as flexible actors in the energy market.

What are digital energy and water efficiency technologies?

The application of data in energy efficiency is driven by a set of interoperable technologies. While innovation is constantly improving performance, this equipment and software are available ‘off the shelf’:

Building Management Systems (BMS): Centralised platforms that collect and control building systems data. BMS integrate HVAC, lighting, security, and fire safety systems to ensure optimal performance and efficiency.

Smart Meters and Submeters: Provide granular insights into electricity, gas, and water consumption, enabling targeted interventions at the zone or equipment level.

Internet of Things (IoT) Devices: Sensors and actuators that monitor occupancy, temperature, humidity, and CO2 levels. These devices feed real-time data into BMS and digital platforms for adaptive control.

Digital Twins and Simulation Software: Virtual replicas of physical buildings used for scenario testing and optimization throughout the building lifecycle. They enable simulation of occupant behaviour, energy flows, Indoor Environmental Quality (IEQ) conditions and environmental impact.

Artificial Intelligence (AI) and Machine Learning (ML): Analyse historical and real-time data to identify patterns and optimize operations. AI can automate fault detection, forecast energy demand, and suggest operational improvements as well as preventive maintenance operations.