Central air conditioning group control system based on dynamic load forecasting and multi-parameter coordination

Abstract

This invention discloses a central air conditioning group control system based on dynamic load forecasting and multi-parameter collaboration, belonging to the field of central air conditioning automation control technology. The system includes a data acquisition unit, an edge computing unit, a distributed execution unit, and a collaborative optimization unit. The data acquisition unit acquires multi-dimensional parameters such as temperature, humidity, concentration, and population density in each area. The edge computing unit models building zones based on a time-space dual-dimensional weighted model, integrates population density and environmental parameters to predict heat load, and generates control commands based on the importance of each area. The collaborative optimization unit constructs a digital twin model to simulate and verify the energy efficiency and comfort of the strategy, and continuously improves the edge prediction accuracy through a model collaboration mechanism between the cloud and the edge. This solution effectively solves the problems of low energy efficiency, slow response, and unfair distribution in traditional group control systems, significantly improving the overall energy efficiency of the system while ensuring the thermal comfort of the group.

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