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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.
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