Most Buildings That Need Retrofits Have No Digital Models to Optimize Them

Digital twins — real-time virtual replicas of physical buildings — can optimize retrofit decisions, predict energy savings, and adaptively control HVAC systems, delivering 30–40% energy efficiency improvements in buildings where they're deployed. But the buildings most urgently needing energy retrofits are precisely the ones that cannot use digital twins: older structures that lack Building Information Models (BIM), sensor networks, or any form of digital representation. The EU's building renovation rate stands at roughly 1% per year, meaning at current pace it would take a century to retrofit the existing building stock. Digital twin technology could accelerate this rate by making retrofit decisions faster, cheaper, and more accurate — but it requires a digital foundation that most buildings simply don't have.

Buildings account for approximately 36% of global energy consumption and nearly 40% of energy-related CO₂ emissions. The IEA reports that despite COP28 pledges, global energy efficiency improvement was only ~1% in 2024, well short of the 4% annual target. The IEA also found that 72% of employers in energy efficiency roles report skilled workforce shortages. The retrofit gap is not primarily a technology problem — the technologies exist — but a decision-support problem: building owners and municipalities cannot cost-effectively determine which retrofits will deliver the best returns for which buildings, especially across large heterogeneous building portfolios.

Most digital twin research and deployment focuses on new construction, where BIM models are created during design. For existing buildings, creating a digital twin requires manual surveying, 3D scanning, sensor installation, and model calibration — a process costing tens of thousands of dollars per building that doesn't scale to portfolios of thousands of structures. Some researchers have explored automated methods using LiDAR and photogrammetry to generate building geometry, but these approaches capture only the envelope, not the internal systems (HVAC, electrical, plumbing) that drive energy performance. Rule-based energy audits are the current standard, but they're labor-intensive, provide only static snapshots, and frequently miss interactions between building systems. The DanRETwin project in Denmark demonstrated a digital twin approach for the Danish building stock, but relied on standardized building archetypes rather than actual building data — sacrificing accuracy for scalability.

A breakthrough likely requires automated, low-cost methods for generating "lightweight" digital twins of existing buildings — models that capture enough building physics for useful retrofit optimization without requiring full BIM detail. This could combine exterior scanning (widely available via aerial imagery and LiDAR), utility billing data (available for most buildings), and occupant feedback into simplified thermal models that improve over time through machine learning. The approach would need to work with heterogeneous building stocks across different eras, construction types, and climate zones. Middleware platforms using standardized protocols (e.g., OPC UA, MQTT) could bridge legacy building management systems with modern digital twin frameworks.

A student team could prototype a "minimal viable digital twin" for an existing campus building using only publicly available data: building footprint from GIS, estimated age and construction type from tax records, weather data from nearby stations, and monthly utility bills. The team would build a simplified thermal model, calibrate it against actual energy use, then simulate the impact of specific retrofit measures (window upgrades, insulation, HVAC replacement). Comparing the model's predictions against actual post-retrofit performance data (available for many university buildings) would quantify the accuracy tradeoff. Skills in building science, data science, and software engineering would be most relevant.