DEDALUS Discoveries | Revolutionising building energy management: AI-driven demand response for a sustainable future

This paper explains how the DEDALUS project leverages AI-driven methodologies to optimize demand response in building energy management, enhancing efficiency and sustainability by dynamically aligning energy consumption with market needs.

Engineering, CARTIF, and Università Politecnica delle Marche, as scientific partners in the DEDALUS project, have published a new paper titled "Methodological approach for optimizing demand response in building energy management through AI-enhanced comfort-based flexibility models."

The paper introduces a novel methodology that leverages artificial intelligence to optimize demand response in building energy management systems, aligning energy consumption dynamically with efficiency objectives.

By analyzing historical data and real-time inputs, AI algorithms forecast energy needs and optimize the performance of critical building systems such as heating, ventilation, air conditioning, and lighting, striking a balance between energy efficiency and operational demands. This methodology is being tested across several pilot projects under the DEDALUS initiative, demonstrating its adaptability to various environments and its capacity to enhance DR strategies.

The AI-driven approach provides significant demand-side flexibility for energy markets, promoting a more sustainable built environment by reducing carbon footprints and optimizing resource use in line with market requirements. The findings underscore AI's transformative potential in building energy management, contributing to a more sustainable and resilient future in energy use.

The paper is available for download at this link.

Contacts
Coordinator:
Diego Arnone, Engineering S.p.A, diego.arnone@eng.it
Communication:

Ilaria Orfino, ICONS, ilaria.orfino@icons.it

Project website: DEDALUS HORIZON
LinkedIn: DEDALUS
YouTube: DEDALUS-EU
Mastodon: https://mastodon.energy/@DEDALUS

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