# Model Predictive HVAC Control with Online Occupancy Model

arXiv: https://arxiv.org/abs/1403.4662 · HTML: https://mlsanity.com/paper/1403.4662/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2014.

## Built on by (most cited first, 2 of 2)

- [Simulation Studies on Deep Reinforcement Learning for Building Control with Human Interaction](https://mlsanity.com/paper/2103.07919/) (2021)
- [Developing and Validating Semi-Markov Occupancy Generative Models: A Technical Report](https://mlsanity.com/paper/2112.11111/) (2021)

## Abstract (arXiv)

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.