Data-Driven Predictive Control for Building Energy Flexibility with Digital Twin Models
Abstract
This paper introduces a data-driven predictive control approach that utilizes digital twin models to achieve building energy flexibility. The study leverages historical building data and digital twin technology to predict energy demand and optimize control strategies for enhancing energy flexibility. By integrating real-time data from digital twins with predictive control algorithms, the proposed approach enables accurate energy demand prediction and enables effective control decisions for energy optimization. Simulation results demonstrate the effectiveness of the data-driven approach in achieving significant energy savings and enabling demand response capabilities in buildings.
References
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