Traditional building management systems relyon manual adjustments and rule-based approaches to controlheating, ventilation, and air conditioning systems. Howeverdespite these efforts, these approaches oftenare ineffective at managing energy consumption due to factorssuch as changing occupancy, external weather conditions, and PPA varying thermal loads.

In contrastto these traditional approaches, AI-powered algorithmscan learn from data on a building's energy consumption patterns to makecustomized adjustments based on data analysis. Bystudying building energy data, AI algorithmscan identify patterns and correlations that arenot immediately apparent to human observers.

There are several waysto apply AI technology in building energy management.

For instance, AI algorithms can predict when a building is likely to experience peak demand for heating or cooling, allowingthe AI system to fine-tune energy usage.

This canresult in significant energy savings andcost savings.