PNNL

Abstract

Predicting indoor temperature conditions are often necessary for advanced building controls. Gray-box models are preferable to do so because they can over- come limitations of white-box and black-box models; however, their current implementation methods can be problematic due to their difficulties in capturing non-linear behaviors and time-varying disturbances. This paper proposes a novel method to implement a popular gray-box model, the 1st-order equivalent thermal parameter (ETP) model. To evaluate performance, we applied the proposed method to predict in- door temperatures for a medium-office building, over the course of one day. Predictions are compared against two existing methods: the ETP model implemented through a common approach and a black-box model. Results suggest that the proposed method can yield more accurate results compared to the other methods; additionally, the proposed method demonstrated similar capabilities in capturing non-linear behaviors as the black-box model.