AI Occupancy-Based HVAC Control

Real-time HVAC optimization based on occupancy patterns and predictions

The Problem

“Wasted HVAC Energy from Uncertain Building Occupancy”

Organizations face these key challenges:

HVAC schedules and setpoints do not match real occupancy, especially with hybrid work and variable space utilization

Limited visibility into zone-level occupancy leads to over-ventilation/over-conditioning and persistent hot/cold complaints

Demand charges and time-of-use rates penalize uncontrolled peaks, but manual tuning cannot reliably shift loads without risking comfort

Impact When Solved

10–25% HVAC energy savings by conditioning only when/where needed5–15% peak demand reduction and measurable demand-charge savings via predictive preconditioning and optimized stagingImproved comfort/IAQ compliance with fewer complaints and reduced equipment wear from smarter cycling control

The Shift

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.