---
abstract: 'This paper presents an occupancy-predicting control algorithm for heating, ventilation, and air conditioning (HVAC) systems in buildings. It incorporates the building’s thermal properties, local weather predictions, and a self-tuning stochastic occupancy model to reduce energy consumption while maintaining occupant comfort. Contrasting with existing approaches, the occupancy model requires no manual training and adapts to changes in occupancy patterns during operation. A prediction-weighted cost function provides conditioning of thermal zones before occupancy begins and reduces system output before occupancy ends. Simulation results with real-world occupancy data demonstrate the algorithm’s effectiveness.'
address: 'Department of Mechanical and Aerospace Engineering, Upson Hall, Cornell University, Ithaca, NY 14853, USA'
author:
- 'Justin R. Dobbs'
- 'Brandon M. Hencey'
bibliography:
- 'library.bib'
- 'mypubs.bib'
title: Model Predictive HVAC Control with Online Occupancy Model
---

model predictive control, MPC, occupancy prediction, on-line training, Markov chains, HVAC

Introduction
============

The long-term increase in energy prices has driven greater interest in demand-based HVAC control. Fixed temperature setpoint schedules and occupancy-triggered operation are commonly used to trim energy consumption, but these approaches have significant drawbacks. First, fixed schedules become outdated; when occupancy patterns change, early or late occupants are left uncomfortable, or the space is conditioned prematurely or 