Self-generating Algorithm of Optimization Strategies for Central Air-conditioning System Based on Multilevel Data-driven Model
In the context of carbon peaking and carbon neutrality goals,optimization strategies for air-conditioning systems have always been highly valued by the industry for its key role in reducing energy consumption during the operation phase of a building.Compared with the traditional empirical rule-based strategy,the supervised optimization control method can achieve more efficient system operation under variable operating conditions.The supervised optimization algorithm,however,consumes more computing power and usually relies on edge computers or cloud servers.In order to avoid the failure of the supervised optimization algorithm due to network or computing device problems,a set of back-up control strategies is needed at the local controller.To solve this problem,this paper proposed a self-generating algorithm for air-conditioning system optimization strategies based on a multilevel data-driven model,which generates regular optimization strategies according to the optimization results of the supervised optimization algorithm through a two-level data-driven model.This paper conducted a case study of an air source heat pump system,built a system performance prediction model based on historical data,selected Q-learning as the supervised optimization algorithm and deployed it in the system prediction model to obtain optimization results.The proposed algorithm was used to generate optimization strategies and verify the energy saving effect.The results showed that compared with original strategies,the strategies generated by the proposed method improved COP by about 5.09％to 7.19％while maintaining a certain level of comfort,which is equivalent to the optimization effect of Q-learning.
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