Quantitative Analysis of Regression-Based Temperature Dynamics Models for Households with A/C Units Subject to Unknown Disturbances

Abstract

This paper focuses on the identification of thermodynamic models for temperature prediction in households. The proposed temperature dynamics model falls under the class of Linear Time-Invariant (LTI) models, making it suitable for model predictive control synthesis. However, the presence of significant and variable thermal disturbances in households adds complexity to the identification process. The performance of various prediction error methods, such as ARX, ARARMAX, and BJ models, along with simplified models incorporating persistent disturbance excitation, is analyzed. The findings highlight the substantial impact of unknown disturbances on temperature predictions, emphasizing the crucial need for accurate prediction of these disturbances for effective household heating and cooling planning. The identification and evaluation of model performance measures are conducted using two months of experimental data collected from five households. This study contributes to understanding of the significance of addressing unknown disturbances and variability in thermodynamic model identification for temperature prediction.