Multi-Objective Predictive Control using Digital Twin Technology for Energy Optimization in Smart Buildings

Justin Pradipta, Edi Leksono, Agus Budiyono

Abstract


This paper presents a multi-objective predictive control framework that utilizes digital twin technology to optimize energy usage in smart buildings. The study aims to simultaneously optimize energy efficiency, occupant comfort, and renewable energy integration. Leveraging the accurate representation of building systems provided by digital twin models, the framework employs advanced predictive control algorithms to make informed decisions for energy optimization. The proposed approach considers diverse objectives and constraints to achieve a well-balanced trade-off between energy efficiency and occupant comfort, while also maximizing the utilization of renewable energy sources.

References


Agostinelli, Sofia, et al. ‘Cyber-Physical Systems Improving Building Energy Management: Digital Twin and Artificial Intelligence’. Energies, vol. 14, no. 8, MDPI, 2021, p. 2338.

O’Dwyer, Edward, et al. ‘Integration of an Energy Management Tool and Digital Twin for Coordination and Control of Multi-Vector Smart Energy Systems’. Sustainable Cities and Society, vol. 62, Elsevier, 2020, p. 102412.

Park, Hyang-A., et al. ‘Digital Twin for Operation of Microgrid: Optimal Scheduling in Virtual Space of Digital Twin’. Energies, vol. 13, no. 20, MDPI, 2020, p. 5504.

Zhang, Hao, et al. "A digital twin-based approach for designing and multi-objective optimization of hollow glass production line." Ieee Access 5 (2017): 26901-26911.


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