基于Q-learning和自适应网络约束的空调节能控制方法研究  

Research on air conditioning energy saving control method based on Q-learning and adaptive network constraints

作　　者：唐杰 林进华 TANG Jie;LIN Jinhua(Gree Electric Appliances,Inc.of Zhuhai Zhuhai 519070)

机构地区：[1]珠海格力电器股份有限公司,广东珠海519070

出　　处：《家电科技》2024年第2期46-50,共5页Journal of Appliance Science & Technology

摘　　要：提出了一种基于Q-learning和自适应网络约束的空调节能控制方法,以解决传统积分微分控制(PID)下空调能耗过高的问题。首先,利用专家系统和Reward函数构建奖励矩阵,并将其元素对应的空调运行参数划分为数据集A和B。其次,初始化径向基函数(Radial Basis Function)神经网络模型,以数据集A为训练数据对建构网络进行训练,使用数据集B对网络约束模型进行验证,直到准确率达到90%以上。最后,将网络约束模型与强化学习的Q-learning策略相结合,实现空调运行的最优策略决策。实验结果表明,在满足用户舒适性的前提下,该算法相较于传统的空调控制逻辑,能够实现节能效果。An energy-efficient control method for air conditioners based on adaptive network constraints and Q-learning is proposed to reduce the high energy consumption of air conditioners under traditional integral differential control.First,the reward matrix is constructed by using the expert system and the Reward function,and the air conditioner operating parameters corresponding to its elements are divided into data sets A and B.Next,the Radial Basis Function(RBF)neural network model is initialized,the constructed network is trained using dataset A as training data,and the network constraint model is validated using dataset B until the accuracy rate reaches more than 90%.Finally,the network constraint model is combined with the Q-learning algorithm to realize the optimal strategy selection for air conditioning energy saving.Experiments show that the algorithm is able to achieve energy saving effect compared to traditional air conditioning control logic without sacrificing user comfort.

关 键 词：专家系统 径向基神经网络 强化学习 舒适节能 

分 类 号：TM925.1[电气工程—电力电子与电力传动] TB6[一般工业技术—制冷工程]