Integrating Digital Twin Technology for Building Energy Flexibility: A Predictive Control Approach

Justin Pradipta, Edi Leksono, Agus Budiyono

Abstract


This paper proposes an integrated framework that combines digital twin technology with predictive control algorithms to enhance building energy flexibility. By leveraging the accurate representation of building systems provided by digital twin models, the framework optimizes energy consumption and demand response in buildings. The study develops predictive control strategies that utilize real-time data from the digital twin to make informed decisions for energy optimization. Simulation results demonstrate the effectiveness of the proposed approach in achieving significant energy savings and enabling demand response capabilities in smart buildings.

References


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Yu, Wei, et al. ‘Energy Digital Twin Technology for Industrial Energy Management: Classification, Challenges and Future’. Renewable and Sustainable Energy Reviews, vol. 161, Elsevier, 2022, p. 112407.

Zheng, Yu, et al. ‘An Application Framework of Digital Twin and Its Case Study’. Journal of Ambient Intelligence and Humanized Computing, vol. 10, Springer, 2019, pp. 1141–1153.


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