Data-driven model for real-time evaluating capacity of commercial buildings to participate in the demand response

Abstract

The collective building stock is a major energy consumer, driving political discussions and forcing building owners to seek out cost-effective technologies. This creates a business opportunity in demand response (DR) services for commercial real estate. Despite recent efforts, developing a simple, scalable model remains an open question. We develop a real-time method for estimating electricity consumption profiles and inertia, enabling a bidding strategy on the DR market without compromising indoor climate. Accurate prediction of temperature changes is achieved through efficient models validated with real data.