家电科技2024，Vol.2Issue(2)：46-50,5.DOI:10.19784/j.cnki.issn1672-0172.2024.02.006
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
唐杰 1林进华1
作者信息
- 1. 珠海格力电器股份有限公司 广东珠海 519070
- 折叠
摘要
Abstract
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Key words
Expert system/Radial basis neural network/Reinforcement learning/Comfortable energy saving分类
信息技术与安全科学引用本文复制引用
唐杰,林进华..基于Q-learning和自适应网络约束的空调节能控制方法研究[J].家电科技,2024,2(2):46-50,5.
