Publication date: October 2016
Source:Building and Environment, Volume 107
Author(s): Donghun Kim, Jie Cai, Kartik B. Ariyur, James E. Braun
Identification approaches applied to semi-physical thermal network structures, so called gray-box modeling approaches, are popular in building science for energy audits, retrofit analysis and advanced building controls, e.g. model predictive control. However conventional identification approaches applied to thermal networks fail when there are significant unmeasured heat gains that influence building responses. This paper presents a method to obtain improved gray-box building models from closed loop data having significant unmeasured disturbances. The method estimates both physical parameters of a building thermal network model and also a disturbance model that characterizes the unmeasured inputs. The performance of the algorithm is demonstrated using numerical and experimental results.
Source:Building and Environment, Volume 107
Author(s): Donghun Kim, Jie Cai, Kartik B. Ariyur, James E. Braun