用预训练变压器实现无需重训的智能空调控制，节能超45%。
HVAC-DPT: A Decision Pretrained Transformer for HVAC Control
- 将空调控制建模为序列预测，用强化学习生成数据预训练变压器
- 在未见建筑上比基线控制器节能45%，且无需额外训练
- 适合需要快速部署、跨建筑通用的智能楼宇能源优化场景
建筑运行占全球能源消耗约40%，其中暖通空调（HVAC）系统能耗最高可达50%。随着未来能源需求上升，提升系统效率对减缓气候变化至关重要。现有控制策略泛化能力差，需大量训练数据和耗时调优，难以快速部署于多样建筑。本文提出 HVAC-DPT，一种基于上下文强化学习的决策预训练变压器，用于多区域空调控制。该模型将控制任务视为序列预测，利用多种强化学习代理生成的交互历史训练因果变压器。通过在推理阶段以上下文方式微调策略，无需修改网络参数即可适应不同建筑，实现零数据迁移部署。在未见过的建筑中，相比基线控制器，能耗降低45%，提供了一种可扩展、高效的低碳解决方案。
原文摘要 · Abstract (English)
Building operations consume approximately 40% of global energy, with Heating, Ventilation, and Air Conditioning (HVAC) systems responsible for up to 50% of this consumption. As HVAC energy demands are expected to rise, optimising system efficiency is crucial for reducing future energy use and mitigating climate change. Existing control strategies lack generalisation and require extensive training and data, limiting their rapid deployment across diverse buildings. This paper introduces HVAC-DPT, a Decision-Pretrained Transformer using in-context Reinforcement Learning (RL) for multi-zone HVAC control. HVAC-DPT frames HVAC control as a sequential prediction task, training a causal transformer on interaction histories generated by diverse RL agents. This approach enables HVAC-DPT to refine its policy in-context, without modifying network parameters, allowing for deployment across different buildings without the need for additional training or data collection. HVAC-DPT reduces energy consumption in unseen buildings by 45% compared to the baseline controller, offering a scalable and effective approach to mitigating the increasing environmental impact of HVAC systems.
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