Thermal Dynamic Models for Predicting the Indoor Temperature of Multi-Zone Buildings

Reducing the energy consumption of buildings is vital for achieving the European Union's climate targets. Towards this direction, one important strategy is to intelligently manage the indoor temperature of buildings to minimize energy con-sumption without compromising the comfort levels of residents. This can be accomplished through model predictive control frameworks that utilize low-complexity thermal dynamic models tailored to the building and heating/cooling system character-istics. This work builds on a single-zone first-order resistance-capacitance (RC) model to develop a multi-zone 1R1C model for the prediction of indoor temperatures in multi-zone buildings. To enhance the prediction accuracy of the derived model, a bimodal multi-zone 1R1C model is further proposed that handles separately the on and off states of the heating system. An estimation framework is then developed based on the least square method to estimate the parameters of the two models using datasets obtained from the EnergyPlus simulator and historical weather data. Simulation results showcase the capability of the proposed models to accurately predict the temperatures of the different zones. The results also demonstrate the superiority of the bimodal multi-zone 1R1C model in terms of prediction accuracy compared to the multi-zone 1R1C model.