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Model-free optimal chiller loading method based on Q-learning

机译：基于Q-Learning的无模型最佳冷却器加载方法

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摘要

Chillers consume considerable energy in building HVAC systems, and the optimal operation of chillers is essential for energy conservation in buildings. This article proposes a model-free optimal chiller loading (OCL) method for optimizing chiller operation. Unlike model-based OCL methods, the proposed method does not require accurate chiller performance models as a priori knowledge. The proposed method is based on the Q-learning method, a classical reinforcement learning method. With the comprehensive coefficient of performance (COP) of chillers as the environmental feedback, the model-free loading controller can learn autonomously and optimize the chiller loading by adjusting the set points of the chilled water outlet temperature. A central chiller plant in an office building located in Shanghai is selected as a case system to investigate the energy conservation performance of the proposed method through simulations. The simulation results suggest that the proposed method can save 4.36% of chiller energy during the first cooling season compared to the baseline control, which is slightly inferior to the value for the model-based loading method (4.95%). Owing to its acceptable energy-saving capability, the proposed method can be applied to central chiller plants that lack a system model and historical data.
机译：冷却器在建筑HVAC系统中消耗相当大的能量，冷却器的最佳运行对于建筑物的节能至关重要。本文提出了一种无模型最佳冷却器加载（OCL）方法，用于优化冷冻机操作。与基于模型的OCL方法不同，所提出的方法不需要准确的冷却器性能模型作为先验知识。该方法基于Q学习方法，一种经典加强学习方法。随着冷却器的综合性能系数（COP）作为环境反馈，无模型装载控制器可以通过调节冷却水出口温度的设定点来自主学习并优化冷却器负荷。位于上海的办公楼中的中央冷水机组被选中为案例系统，以通过模拟研究所提出的方法的节能性能。仿真结果表明，与基线控制相比，该方法可以在第一个冷却季节节省4.36％的冷却能量，这略不如基于模型的装载方法（4.95％）。由于其可接受的节能能力，所提出的方法可以应用于缺乏系统模型和历史数据的中央冷却器植物。

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