Data-driven Thermal Model Inference with ARMAX, in Smart Environments, based on Normalized Mutual Information

TL;DR

This paper introduces a novel data-driven thermal modeling method for smart buildings that combines ARMAX with Normalized Mutual Information to identify key environmental influences on indoor temperature.

Contribution

It proposes a new approach integrating NMI with ARMAX for more accurate thermal model inference in smart environments, addressing stochastic disturbances.

Findings

NMI-guided ARMAX outperforms traditional models

The method effectively identifies dominant environmental inputs

Validation on three datasets shows improved accuracy

Abstract

Understanding the models that characterize the thermal dynamics in a smart building is important for the comfort of its occupants and for its energy optimization. A significant amount of research has attempted to utilize thermodynamics (physical) models for smart building control, but these approaches remain challenging due to the stochastic nature of the intermittent environmental disturbances. This paper presents a novel data-driven approach for indoor thermal model inference, which combines an Autoregressive Moving Average with eXogenous inputs model (ARMAX) with a Normalized Mutual Information scheme (NMI). Based on this information-theoretic method, NMI, causal dependencies between the indoor temperature and exogenous inputs are explicitly obtained as a guideline for the ARMAX model to find the dominating inputs. For validation, we use three datasets based on building energy systems-against which we compare our method to an autoregressive model with exogenous inputs (ARX), a regularized ARMAX model, and state-space models.