# A Markov Process Approach to Ensemble Control of Smart Buildings

## TL;DR

This paper introduces a Markov Process method for modeling and controlling energy consumption in smart buildings, simplifying complex physics models and enabling real-time data integration for optimal probabilistic management.

## Contribution

It presents a novel procedure to convert physical building models into Markov Processes, reducing complexity and parameter dependence while supporting real-time data integration and control.

## Key findings

- Reduced model complexity compared to traditional thermo-physics models
- Enabled real-time data integration for system-level analysis
- Validated approach using Belgian building data

## Abstract

This paper describes a step-by-step procedure that converts a physical model of a building into a Markov Process that characterizes energy consumption of this and other similar buildings. Relative to existing thermo-physics-based building models, the proposed procedure reduces model complexity and depends on fewer parameters, while also maintaining accuracy and feasibility sufficient for system-level analyses. Furthermore, the proposed Markov Process approach makes it possible to leverage real-time data streams available from intelligent data acquisition systems, which are readily available in smart buildings, and merge it with physics-based and statistical models. Construction of the Markov Process naturally leads to a Markov Decision Process formulation, which describes optimal probabilistic control of a collection of similar buildings. The approach is illustrated using validated building data from Belgium.