ICANN 98 pp 875-880 | Cite as

The Adaptive Setback Thermostat

  • Olle Gällmo
  • Patrik Lögdahl
Conference paper
Part of the Perspectives in Neural Computing book series (PERSPECT.NEURAL)


We present an adaptive setback thermostat (AST) which switches between two temperature setpoints — one optimized for user comfort and one for saving energy. The AST operates locally in an office room and makes its decisions based on how the room is (expected to be) used. Core issues, in decreasing order of importance, are user comfort, user friendliness (ease of installation and use) and to reduce energy costs. It is argued why a reinforcement learning approach may not be the best solution, and then shown how to reformulate the problem using a simple heuristic where reward maximization is replaced by explicit prediction of user arrivals, using temporal difference learning.


Motion Detector State Transition Probability Mode Selector Reduce Energy Cost Movement Predictor 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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    Mozer MC, Vidmar L, Dodier RH. The Neurothermostat: Predictive optimal control of residential heating systems. In: Mozer MC, Jordan MI, Petsche T (Eds), Advances in Neural Information Processing Systems 9, MIT Press, 1997, pp 953–959Google Scholar
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    Sutton RS, Barto AG. Reinforcement Learning: An Introduction, MIT Press, 1998.Google Scholar
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    Sutton RS. Learning to Predict by the Methods of Temporal Difference, Machine Learning 1988; 3:9–44, Kluwer Academic PublishersGoogle Scholar
  4. [4]
    Lögdahl P. The Adaptive Setback Thermostat: Experiments in simulated and real office environments. MSc Thesis, Dept. of Computer Systems, Uppsala University, Sweden, 1998Google Scholar

Copyright information

© Springer-Verlag London 1998

Authors and Affiliations

  • Olle Gällmo
    • 1
  • Patrik Lögdahl
    • 1
  1. 1.Department of Computer SystemsUppsala UniversityUppsalaSweden

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