一种基于监督学习的自适应空调优化控制方案

Adaptive optimal control scheme of HVAC system based on supervised learning algorithm

提出了一种实时优化控制方案,将机器学习领域的监督学习算法应用于空调优化节能控制。与基于半物理模型的优化控制相比,该方案可以采用简单的机器学习模型,并可以在线学习更新,以适应实际应用中的系统老化和传感器误差等问题。基于某摩天大楼的冷却塔系统,进行了动态模拟测试,并与基于半物理模型的优化控制进行了比较,结果表明该方案有显著优势。

Proposes a real-time adaptive control scheme of applying supervised learning algorithms to the control of HVAC systems. Comparing with the semi-physical model optimal control, the proposed method can make use of simple machine learning models and be automatically updated online, so as to adapt to system degradation and/or sensor errors. Conducts the dynamic validation tests for the cooling tower system in a high-rise building. The results show that the proposed scheme has significant advantages over the semi-physical model based on optimal control method.