As concerns about climate change, energy efficiency, and sustainability continue to growcontinue to increase, buildings have become a major focusin efforts to reduce energy consumption. One innovative solutionis found in the application of machine learning technology to optimize building energy consumption.
Traditional building management systems relyon manual adjustments and rule-based approaches to controlcritical building systems and infrastructure. Howeverin large part, these approaches oftenlead to energy waste and inefficiency due to factorslike occupancy rates, environmental conditions, and thermal fluctuations.
In contrastwith more conventional methods, AI-powered algorithmscan learn from data on a building's energy consumption patterns to makereal-time adjustments and recommendations. Bystudying building energy data, AI algorithmscan identify patterns and correlations that arenot immediately apparent to human observers.
There are several waysto leverage AI algorithms for energy efficiency.
For instancewith AI algorithms, PPA peak energy usage can be anticipated and adjusted, allowingthem to adjust temperatures and energy consumption accordingly.
This canhelp to minimize energy consumption andreduced energy costs.
