Scalable Framework for Delivering Demand Flexibility from Commercial Buildings Using Machine Learning

A growing number of utilities are looking to commercial buildings as a source of flexible grid capacity, but scaling demand flexibility across diverse building portfolios remains a challenge. This paper explores how machine-learning forecasting, cloud-based optimization, and automated controls can make building flexibility more reliable, repeatable, and cost-effective. Drawing on data from a public elementary school near Seattle, the analysis compares machine-learning forecasts with a commonly used utility baseline and demonstrates how improved forecasting accuracy can help unlock greater value for both utilities and building owners.

In this white paper you will learn:

• Key barriers that have historically limited the scale of commercial building demand flexibility programs.
• What makes a demand flexibility solution scalable across large and diverse building portfolios.
• How forecasting needs differ between real-time operations and day-ahead planning.
• Why HVAC scheduling data can significantly improve forecasting accuracy.
• How automated demand flexibility strategies can support both grid needs and customer objectives.
• How more accurate forecasts can increase confidence in commercial buildings as a reliable grid resource.