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The "AI-Driven Smart Energy-Saving Management System for Chiller Groups" adopts AI intelligence and proactive control strategies to fully replace traditional manual operations, achieving system-level smart management. Applicable to single or multi-chiller group controls and related motor-driven power systems, this system aims to enhance the overall Coefficient of Performance (COP) of power equipment, including chillers, water pumps, and cooling towers. In practice, the system establishes individual AI energy consumption simulations and digital twin prediction models for each chiller and its peripheral water-side equipment. Driven by dual machine learning models in the cloud, it comprehensively evaluates real-time cooling load demands, individual chiller COPs, and health indicators. This dynamically generates optimal control decisions to precisely configure the startup/shutdown sequences, load allocations, and backup strategies of the chillers.

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