Assessing the relevance of reduced order models for building envelope

The paper focuses on parameter estimation processes for physically meaningful models tuned online and de-fine a process to determine whether a model is rele-vant or not for GMBA-BEMS tuning purpose. The proposed approach relies on the data coming from the PREDIS/MHI platform. The first step is to cal-culate realistic parameters with possible intervals be-cause nonlinear optimization, required for physically explicit models, implies initial parameters. The next step is to find the best reduced order model structure using an iterative nonlinear optimization approach us-ing recorded data that leads to parameter estimation. It is based on randomize initial values for parameters to measure the convexity of the search space in study-ing the convergence. Finally, the last step consists in enhancing the time zones where reduced order model does not fit well with the available data. It points out some non-modeled phenomena. It is based on a weighted iterative estimation method where weights depend on the estimation errors obtain at the previous step.