Predictive Energy Optimization (PEO) is software platform designed to improve the energy-efficient operations of large, complex buildings, whether commercial, public, or academic. Running as a software-as-service (SaaS), PEO optimizes around system efficiency, occupancy comfort and lowest cost. Energy reductions in the range of 10-25% are typical, with reductions climbing to as high as 40% during operational peaks.

Driven by Machine Learning

  • Unique thermal model for every building
  • Machine learning: an ever-evolving model
  • Model evolves with the building through seasons and various modes of operation
  • Capitalizes on every building’s natural thermal properties
  • Incorporates multiple data streams including energy, weather, utility pricing, and BMS points
  • Automatic discovery and learning produces truly customized models regardless of equipment types and operations

Closed-Loop Control

  • Optimized for Cost and Comfort
  • Micro-adjustments made throughout the day to zone temperatures and pressures
  • Shifts HVAC load to less expensive hours
  • Ensures comfort with individual temperature requirements for each zone
  • Secure and local
  • Dynamic setpoints delivered via on-site appliance
  • Measured for Efficacy
  • Energy, comfort and operations impact measured
  • Performance continuously verified via AM&V
  • Incorporates real-time occupant feedback (future feature)