Autonomous HVAC Control, A Reinforcement Learning Approach. Barrett, E. & Linder, S. In Bifet, A., May, M., Zadrozny, B., Gavalda, R., Pedreschi, D., Bonchi, F., Cardoso, J., & Spiliopoulou, M., editors, Machine Learning and Knowledge Discovery in Databases, of Lecture Notes in Computer Science, pages 3–19, Cham, 2015. Springer International Publishing. doi abstract bibtex Recent high profile developments of autonomous learning thermostats by companies such as Nest Labs and Honeywell have brought to the fore the possibility of ever greater numbers of intelligent devices permeating our homes and working environments into the future. However, the specific learning approaches and methodologies utilised by these devices have never been made public. In fact little information is known as to the specifics of how these devices operate and learn about their environments or the users who use them. This paper proposes a suitable learning architecture for such an intelligent thermostat in the hope that it will benefit further investigation by the research community. Our architecture comprises a number of different learning methods each of which contributes to create a complete autonomous thermostat capable of controlling a HVAC system. A novel state action space formalism is proposed to enable a Reinforcement Learning agent to successfully control the HVAC system by optimising both occupant comfort and energy costs. Our results show that the learning thermostat can achieve cost savings of 10% over a programmable thermostat, whilst maintaining high occupant comfort standards.
@inproceedings{barrett_autonomous_2015,
address = {Cham},
series = {Lecture {Notes} in {Computer} {Science}},
title = {Autonomous {HVAC} {Control}, {A} {Reinforcement} {Learning} {Approach}},
isbn = {978-3-319-23461-8},
doi = {10.1007/978-3-319-23461-8_1},
abstract = {Recent high profile developments of autonomous learning thermostats by companies such as Nest Labs and Honeywell have brought to the fore the possibility of ever greater numbers of intelligent devices permeating our homes and working environments into the future. However, the specific learning approaches and methodologies utilised by these devices have never been made public. In fact little information is known as to the specifics of how these devices operate and learn about their environments or the users who use them. This paper proposes a suitable learning architecture for such an intelligent thermostat in the hope that it will benefit further investigation by the research community. Our architecture comprises a number of different learning methods each of which contributes to create a complete autonomous thermostat capable of controlling a HVAC system. A novel state action space formalism is proposed to enable a Reinforcement Learning agent to successfully control the HVAC system by optimising both occupant comfort and energy costs. Our results show that the learning thermostat can achieve cost savings of 10\% over a programmable thermostat, whilst maintaining high occupant comfort standards.},
language = {en},
booktitle = {Machine {Learning} and {Knowledge} {Discovery} in {Databases}},
publisher = {Springer International Publishing},
author = {Barrett, Enda and Linder, Stephen},
editor = {Bifet, Albert and May, Michael and Zadrozny, Bianca and Gavalda, Ricard and Pedreschi, Dino and Bonchi, Francesco and Cardoso, Jaime and Spiliopoulou, Myra},
year = {2015},
keywords = {Bayesian learning, HVAC control, Reinforcement learning},
pages = {3--19},
}
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