Predicting System Performance with Uncertainty

Abstract

The main purpose of this research is to include uncertainty that lies in modeling process and that arises from input values when predicting system performance, and to incorporate uncertainty related to system controls in a computationally inexpensive way. We propose using Gaussian Processes for system performance predictions and explain the types of uncertainties included. As an example, we use a Gaussian Process to predict chilled water use and compare the results with Neural Network. As an initial step of our research, we examine how variation in AHU supply air temperature affects chilled water use in summer time. We briefly discuss the advantages of our proposed method and future research topics in the concluding remarks.