To solve the problem of unstable performance and inefficient training process of low-quality data conditions at the initial stage of online deployment of air conditioner scheduling,we propose a migration-imitation learning-based air conditioning scheduling strategy simulation method.Reinforcement learning methods are used to generate building operation strategies.A standard building simulation model serves as the source domain,upon which migration learning is applied.An imitation learning loss function is incorporated into the intelligent loss function to enhance algorithm performance.The results indicate that,compared with the non-use of migration learning,the proposed method can improve the operational efficiency by 16.2％,effectively resolving the operational instability issues at the initial stage of reinforcement learning training.Compared to methods without imitation learning,operational efficiency is enhanced by 11.5％,significantly improving the training efficiency of reinforcement learning.
关键词
迁移学习/强化学习/模仿学习/空调调控方法/室温控制
Key words
transfer learning/reinforcement learning/imitation learning/air conditioning control/room temperature control
维普
万方数据