Peter Stone's Selected Publications

Classified by TopicClassified by Publication TypeSorted by DateSorted by First Author Last NameClassified by Funding Source


A Learning Agent for Heat-Pump Thermostat Control

Daniel Urieli and Peter Stone. A Learning Agent for Heat-Pump Thermostat Control. In Proceedings of the 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS), May 2013.

Download

[PDF]426.8kB  [postscript]1.0MB  

Abstract

Heating, Ventilation and Air Conditioning (HVAC) systems are one of the biggest energy consumers around the world. With the efforts of moving to sustainable energy consumption, heat-pump based HVAC systems have gained popularity due to their high efficiency and due to the fact that they are powered by electricity rather than by gas or oil. One drawback of heat-pump systems is that their efficiency sharply decreases when the outdoor temperature is around or below freezing. Therefore, they are backed up by an auxiliary heating system that is effective in cold whether, but that consumes twice as much energy. A popular way of saving energy in HVAC systems is setting back the thermostat, meaning, relaxing the heating/cooling requirements when occupants are not at home. While this practice leads to significant energy savings in many systems, it could in fact increase the energy consumption in a heat-pump based system, using existing control strategies, as it forces an excessive usage of the auxiliary heater. In this paper, we design and implement a complete, adaptive reinforcement learning agent which applies a new control strategy for a heat-pump thermostat. For our experiments, we use a complex, realistic simulator that was developed for the US Department of Energy. Results show that the learned control strategy (1) leads to roughly 7.0\%-14.5\% energy savings in typical homes in the New York City, Boston, and Chicago areas; while (2) keeping the occupants' comfort level unchanged when compared to an existing strategy that is deployed in practice.

BibTeX Entry

@InProceedings{AAMAS13-urieli,
  author = {Daniel Urieli and Peter Stone},
  title = {A Learning Agent for Heat-Pump Thermostat Control},
  booktitle = {Proceedings of the 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS)},
  location = {Saint Paul, Minnesota, USA},
  month = {May},
  year = {2013},
  abstract = "
              Heating, Ventilation and Air Conditioning (HVAC) systems
              are one of the biggest energy consumers around the
              world. With the efforts of moving to sustainable energy
              consumption, heat-pump based HVAC systems have gained
              popularity due to their high efficiency and due to the
              fact that they are powered by electricity rather than by
              gas or oil.  One drawback of heat-pump systems is that
              their efficiency sharply decreases when the outdoor
              temperature is around or below freezing. Therefore, they
              are backed up by an auxiliary heating system that is
              effective in cold whether, but that consumes twice as
              much energy. A popular way of saving energy in HVAC
              systems is \emph{setting back} the thermostat, meaning,
              relaxing the heating/cooling requirements when occupants
              are not at home. While this practice leads to
              significant energy savings in many systems, it could in
              fact increase the energy consumption in a heat-pump
              based system, using existing control strategies, as it
              forces an excessive usage of the auxiliary heater. In
              this paper, we design and implement a complete, adaptive
              reinforcement learning agent which applies a new control
              strategy for a heat-pump thermostat. For our
              experiments, we use a complex, realistic simulator that
              was developed for the US Department of Energy.  Results
              show that the learned control strategy (1) leads to
              roughly \textbf{7.0\%-14.5\%} energy savings in typical
              homes in the New York City, Boston, and Chicago areas;
              while (2) keeping the occupants' comfort level unchanged
              when compared to an existing strategy that is deployed
              in practice.
  ",
}

Generated by bib2html.pl (written by Patrick Riley ) on Fri Aug 15, 2014 16:26:03