Adaptive regression model-based real-time optimal control of central air-conditioning systems

Model-based, real-time optimal control is an effective tool to improve the energy efficiency of central air-conditioning systems. However, its performance relies heavily on the accuracy of the system models, whereas the development of accurate models for central air-conditioning systems is not easy due to their complex dynamics and non-linearities. This study presents an adaptive regression model-based real-time optimal control strategy for central air-conditioning systems. In the proposed strategy, regression models are adopted to describe the relationship between the power consumption of the system and the variables that are optimised. Their simple structures enable a low computation load for model updating and real-time optimisation. The length of the training data (for model updating) is investigated, and a suitable length is found using a similarity check-based method. Case studies were carried out to assess the performance of the proposed strategy, and they demonstrated that (1) a week was the optimal length of the training data for the case system, (2) the proposed strategy saved energy use by 3.48-10.59% when compared with a benchmark system with no optimisation, and (3) the proposed method reduced the computational load by 85% when compared with a simplified physical model-based optimal control without adaptive modelling.