Calibration and validation of physics-based data-driven models for simulating the thermal behavior of indoor spaces in an assisted living facility

Abstract

A case study represented by an assisted living facility in Norway is modeled utilizing physics-based data-driven digital twin (DT) of the indoor thermal spaces with indoor temperature. Autoregressive Distributed Lag (ARDL), Machine Learning (ML), and Non-linear Autoregressive (NARX) models with timeseries and sliding-window cross-validation are compared. Results show that NARX models have the highest accuracy, with a MAPE score of 0.03%. In addition, the sliding-window enhanced the models’ accuracy and reduced the cyclical pattern for the autocorrelated values. The HVAC systems in this study case are representative of those found in Norwegian buildings, making the digital twin calibration applicable to other facilities.