# Analyzing Occupancy-Driven Thermal Dynamics in Smart Buildings

## TL;DR

This paper introduces a probabilistic modeling framework for analyzing thermal dynamics and evaluating heating strategies in smart buildings, aiming to optimize energy efficiency while maintaining occupant comfort.

## Contribution

It presents a novel Markov reward-based framework for modeling occupancy-driven thermal dynamics and compares multiple heating strategies using probabilistic model checking.

## Key findings

- Selective heating strategy is 13.5 times more cost-effective.
- Framework effectively evaluates and compares control strategies.
- Occupancy patterns significantly influence thermal management efficiency.

## Abstract

The fact that a proper HVAC control strategy can reduce the energy consumption of a building by up to 45% has driven significant research in demand-based HVAC control. This paper presents a novel framework for modeling and analysis of thermal dynamics in smart buildings that incorporates building's thermal properties, a stochastic occupancy model and heating strategies. Each zone of a building is modeled with the help of discrete time Markov rewards formalism where the states represent the occupancy of that zone (either occupied or empty), and the state rewards incorporate the thermal dynamics and heating strategy. To demonstrate the applicability of our proposed framework, we evaluate and compare six different heating strategies for the two zone scenario of a university building. The obtained quantitative results from the PRISM probabilistic model checker show that one of the evaluated control strategies (viz. selective strategy) satisfies our requirement in terms of maintaining the occupants' comfort while being up to 13.5 times more cost effective when compared to the other evaluated strategies. Such evaluations demonstrate the framework's ability to assist in selecting the control strategy tailored around the occupancy pattern and building's thermal property.