AI Building Energy Modeling

AI-enhanced building energy simulation and modeling for design optimization

The Problem

“Slow, costly building energy modeling limits decisions”

Organizations face these key challenges:

High cost and long lead times to build and calibrate accurate energy models for each building, especially when drawings and equipment inventories are incomplete

Inconsistent results across modelers and tools, making retrofit prioritization and savings guarantees risky for owners, utilities, and ESCOs

Difficulty maintaining valid baselines as operations, occupancy, weather, and controls change, leading to disputed savings and missed anomalies

Impact When Solved

Portfolio-scale modeling: cut per-building modeling cycle time by 50–80% while improving calibration quality using meter/BAS dataBetter capital allocation: increase retrofit ROI by 10–25% by prioritizing measures with higher confidence savings and lower performance riskOperational performance: deliver 5–15% energy reduction and 5–10% peak demand reduction via continuous model-driven fault detection and optimization

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.

Time-SeriesEmerging Standard

8.5

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.