Methodological Approach for Optimizing Demand Response in Building Energy Management through AI-Enhanced Comfort-Based Flexibility Models

This paper presents a comprehensive methodology aimed at optimizing Demand Response (DR) within building energy management systems. The focus is on integrating Artificial Intelligence (AI)-enhanced comfort-based flexibility models to dynamically align energy consumption patterns with individual comfort preferences. By analyzing historical data and real-time inputs, AI algorithms forecast energy and comfort needs and optimize critical building systems. The investigation encompasses diverse geographical pilots within the DEDALUS initiative, highlighting unique applications and outcomes. This proactive DR enhances market participation, providing demand-side flexibility within occupant comfort limits, contributing to a sustainable built environment.