Smart Building Clusters Optimize Demand Response Through Load Targeting

⚡ AI Executive Summary

Researchers have developed a new method for formulating optimal load targets for clusters of smart buildings, addressing the challenge of coordinating diverse, time-coupled adjustable loads to unlock demand response potential. The approach uses machine learning and mathematical optimization to create realistic load targets that reflect what building clusters can actually achieve. This advance matters for grid operators and utilities because it bridges the gap between theoretical demand flexibility and practical response capabilities. By more accurately forecasting and decomposing load adjustment targets, utilities can better coordinate building-level resources for grid support—whether for peak shaving, frequency regulation, or renewable integration. As building electrification accelerates and demand-side resources become critical to grid operations, methods that improve the reliability and granularity of aggregated load control will be essential to realizing the full value of distributed flexibility.