Multi-level identification performance for RC-based control-oriented model of the UK office archetype

Abstract

Resistance-capacitance-based grey-box models are widely adopted as one of the modelling solutions in model-predictive controls. These models have been evaluated to determine the optimal level of complexity in standardised cases. However, further evaluations are needed to draw more universal conclusions across diverse scenarios, modelling approaches, and operational conditions. In this study, a series of grey-box models were identified by MPCPy based on a British office model, followed by a parametric analysis on model format, modelling details, training data volume, and validation periods. The R2C2 model yielded the most accurate predictions with less deviations, and more accurate estimations were observed in multi-zone models. Additionally, it is suggested to consider direct normal irradiance as a modelling input in multi-zone models, and adaptive re-calibrations are recommended when significant changes in solar radiations occur