Implementation of System Identification Techniques and Optimal Control for RC Model Selection by Means of TRNSYS Simulation Results and Experimental Data

Abstract

Simulating the thermal model of a district requires simultaneously retaining accuracy and simplicity, in order to avoid cumbersome calculations and unrealistic predictions. Within this scope, introducing a simple structure for modeling the energy consumption of a building that can be expanded to the district level becomes essential. In this paper, a hierarchy of thermal models with increasing complexity is developed to identify the simplest structure that can effectively represent the thermal behavior of a building, using a simulated building in TRNSYS and the measurements of a real building as two datasets to estimate the model parameters. Each model is placed in a closed loop system to track the constant indoor temperature by means of the linear quadratic regulator (LQR) technique. To select the best structure, the model outputs are compared to TRNSYS simulations and measurements. The main features of the selected models include the use of only a few parameters to predict the indoor temperature, peak power, total heat demand, and transient behavior of a building. It is shown that the proposed models are able to determine the indoor temperature with less than 1 °C of error, and the total heat demand and peak power are also determined with an error lower than 25%.