AI Building Retrofit Optimization
Machine learning for identifying and prioritizing energy retrofit opportunities
The Problem
“Optimize building retrofits for lowest energy cost”
Organizations face these key challenges:
Retrofit packages are evaluated in small numbers due to manual modeling effort, leading to suboptimal measure selection and missed interactive effects (e.g., envelope + heat pump sizing + controls).
Savings uncertainty is high because baselines rely on limited data and simplified assumptions, causing frequent shortfalls in M&V and performance-contract disputes.
Tariff complexity, demand charges, and incentive eligibility (utility programs, IRA/REBATES, local carbon ordinances) are hard to model consistently across sites, delaying projects and reducing captured value.
Impact When Solved
Real-World Use Cases
AI in Energy Industry: Smart Grid Optimization and Energy Management
This is like giving the entire power system—power plants, grids, and large customers—a real‑time ‘autopilot’ that constantly predicts demand, reroutes electricity, and tunes equipment so you use less fuel, waste less energy, and keep the lights on more reliably.
Artificial Intelligence for Energy Systems
Think of this as a playbook of AI tricks for running power systems—generation, grids, and consumption—more like a smart thermostat and less like a manual on/off switch. It applies machine learning to decide how much power to produce, when to store it, and how to route it so the overall system is cheaper, cleaner, and more reliable.