Abstract
We propose a reinforcement learning approach to heating control in home automation, that can acquire a set of rules enabling an agent to heat a room to the desired temperature at a defined time while conserving as much energy as possible. Experimental results are presented that show the feasibility of our method.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Anderson, C., Hittle, D., Ketchmar, R., Young, P.: Robust reinforcement learning for heating, ventilation, and air conditioning control of buildings. In: Si, J., Barto, A., Powell, W., Wunsch, D. (eds.) Learning and Approximate Dynamic Programming, ch. 20, pp. 517–534. D., John Wiley & Sons (2004)
Ellis, C., Hazas, M., Scott, J.: Matchstick: A room-to-room thermal model for predicting indoor temperature from wireless sensor data. In: Proc. of IPSN 2013 (2013)
Krödel, M.: Autonome Optimierung des Verhaltens von Fahrzeugsteuerungen auf der Basis von Verstärkungslernen. Ph.D. thesis, Universität Siegen, Germany (2006)
Mozer, M.C.: The neural network house: An environment that adapts to its inhabitants. In: Coen, M. (ed.) Proc. of the AAAI Spring Symposium on Intelligent Environments, pp. 110–114. AAAI Press (1998)
Rummery, G.A., Niranjan, M.: On-line q-learning using connectionist systems. Tech. rep., University of Cambridge, Department of Engineering (1994)
Sutton, R.S., Barto, A.G. (eds.): Reinforcement Learning: An Introduction (Adaptive Computation and Machine Learning). The Mit Press (1998)
WAGO Kontakttechnik GmbH & Co. KG., http://www.wago.us
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zenger, A., Schmidt, J., Krödel, M. (2013). Towards the Intelligent Home: Using Reinforcement-Learning for Optimal Heating Control. In: Timm, I.J., Thimm, M. (eds) KI 2013: Advances in Artificial Intelligence. KI 2013. Lecture Notes in Computer Science(), vol 8077. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40942-4_30
Download citation
DOI: https://doi.org/10.1007/978-3-642-40942-4_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40941-7
Online ISBN: 978-3-642-40942-4
eBook Packages: Computer ScienceComputer Science (R0)