Sensitivity and robustness of end-to-end data-driven approach for building performance optimization

Abstract

This paper discusses an approach to optimizing the performance of an end-to-end data-driven control approach for building energy management. The proposed approach, designed for systems that exhibit non-stationary behavior, involves two primary components: (1) performance degradation detection, followed by (2) relearning a set of data-driven models of the system to update the controller policy using a reinforcement learning approach. The overall control framework involves a large hyperparameter space that has to be tuned for "optimal"performance. In this paper, we analyze the sensitivity and robustness to a small set of relevant hyperparameters that have a significant impact on the overall performance. We study the performance in terms of the accuracy of the derived data-driven models that support relearning and the speed of convergence of the reinforcement learning controller.