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首页> 外文期刊>Automation Science and Engineering, IEEE Transactions on >Event-Based Optimization Within the Lagrangian Relaxation Framework for Energy Savings in HVAC Systems
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Event-Based Optimization Within the Lagrangian Relaxation Framework for Energy Savings in HVAC Systems

机译：拉格朗日松弛框架内基于事件的优化，用于HVAC系统的节能

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摘要

Optimizing HVAC operation becomes increasingly important because of the rising energy cost and comfort requirements. In this paper, an innovative event-based approach is developed within the Lagrangian relaxation framework to minimize an HVAC's day-ahead energy cost. To solve the HVAC optimization problem based on events is challenging since with time-dependent uncertainties in weather, cooling load, etc., the optimal policy is not stationary. The nonstationary policy space is extremely large, and it is time consuming to find the optimal policy. To overcome the challenge, we develop an event-based approach to make the nonstationary optimal policy stationary in the planning horizon. The key idea is to augment state variables to include the time-dependent variables that make the optimal policy nonstationary and then define events based on the extended state variables. In addition, we develop within the Lagrangian relaxation framework a Q-learning method where Q-factors are used to evaluate event-action pairs and to obtain the optimal policy. Numerical results demonstrate that, as compared with time-based approaches, the event-based approach maintains similar levels of energy costs and human comfort, but reduces computational efforts significantly and has a much faster response to events.
机译：由于不断增长的能源成本和舒适度要求，优化HVAC运行变得越来越重要。在本文中，在拉格朗日松弛框架内开发了一种基于事件的创新方法，以最大程度地减少HVAC的日间能耗。基于事件来解决HVAC优化问题具有挑战性，因为天气，制冷负荷等随时间变化的不确定性，最优策略并不是一成不变的。非平稳策略空间非常大，找到最佳策略非常耗时。为了克服挑战，我们开发了一种基于事件的方法，以使非平稳最优策略在计划范围内保持不变。关键思想是增加状态变量以包括使最佳策略不稳定的时间相关变量，然后根据扩展状态变量定义事件。此外，我们在拉格朗日松弛框架内开发了一种Q学习方法，其中Q因子用于评估事件－动作对并获得最佳策略。数值结果表明，与基于时间的方法相比，基于事件的方法保持了相似的能源成本和人类舒适度，但显着减少了计算工作量，并且对事件的响应速度更快。

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