Bayesian Learning of Consumer Preferences for Residential Demand Response

Abstract: In coming years residential consumers will face real-time electricity tariffs with energy prices varying day to day, and effective energy saving will require automation - a recommender system, which learns consumer's preferences from her actions. A consumer chooses a scenario of home appliance use to balance her comfort level and the energy bill. We propose a Bayesian learning algorithm to estimate the comfort level function from the history of appliance use. In numeric experiments with datasets generated from a simulation model of a consumer interacting with small home appliances the algorithm outperforms popular regression analysis tools. Our approach can be extended to control an air heating and conditioning system, which is responsible for up to half of a household's energy bill.

[1]  Hamed Mohsenian Rad,et al.  Optimal Residential Load Control With Price Prediction in Real-Time Electricity Pricing Environments , 2010, IEEE Transactions on Smart Grid.

[2]  Kenneth N. Brown,et al.  Learning Individual Thermal Comfort using Robust Locally Weighted Regression with Adaptive Bandwidth , 2012 .

[3]  C. Monteiro,et al.  Optimum residential load management strategy for real time pricing (RTP) demand response programs , 2012 .

[4]  Manuela M. Veloso,et al.  Learned Behaviors of Multiple Autonomous Agents in Smart Grid Markets , 2011, AAAI.

[5]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[6]  Alexander J. Smola,et al.  Support Vector Regression Machines , 1996, NIPS.

[7]  Chen Wu,et al.  Hierarchical preference learning for light control from user feedback , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.

[8]  Eyke Hüllermeier,et al.  Preference Learning , 2005, Künstliche Intell..

[9]  Eyke Hllermeier,et al.  Preference Learning , 2010 .

[10]  D. McFadden,et al.  MIXED MNL MODELS FOR DISCRETE RESPONSE , 2000 .

[11]  Mohammed H. Albadi,et al.  A summary of demand response in electricity markets , 2008 .

[12]  Louis F Lopez.  A sensor toolkit to facilitate context-awareness for just-in-time questioning , 2005 .

[13]  Irina O. Volkova,et al.  Active consumer: Optimization problems of power consumption and self-generation , 2014, Automation and remote control.

[14]  Kevin P. Murphy,et al.  Machine learning - a probabilistic perspective , 2012, Adaptive computation and machine learning series.

[15]  Martha Martins Carvalho,et al.  Demand response models with correlated price data: A robust optimization approach , 2012 .

[16]  Karl Henrik Johansson,et al.  Scheduling smart home appliances using mixed integer linear programming , 2011, IEEE Conference on Decision and Control and European Control Conference.

[17]  Sven Seuken,et al.  An Active Learning Approach to Home Heating in the Smart Grid , 2013, IJCAI.

[18]  Pramode K. Verma,et al.  Predicting user comfort level using machine learning for Smart Grid environments , 2011, ISGT 2011.

[19]  J. Neumann,et al.  Theory of games and economic behavior , 1945, 100 Years of Math Milestones.

[20]  Farrokh Albuyeh,et al.  Grid of the future , 2009, IEEE Power and Energy Magazine.

[21]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[22]  Wei Chu,et al.  Preference learning with Gaussian processes , 2005, ICML.

[23]  Juan M. Morales,et al.  Real-Time Demand Response Model , 2010, IEEE Transactions on Smart Grid.

[24]  H. Farhangi,et al.  The path of the smart grid , 2010, IEEE Power and Energy Magazine.

[25]  Tianqi Chen,et al.  Higgs Boson Discovery with Boosted Trees , 2014, HEPML@NIPS.

[26]  Yik-Chung Wu,et al.  Load/Price Forecasting and Managing Demand Response for Smart Grids: Methodologies and Challenges , 2012, IEEE Signal Processing Magazine.

[27]  Václav Kaczmarczyk,et al.  Optimal Scheduling of Domestic Appliances via MILP , 2014 .

[28]  Wolfgang Ketter,et al.  Demand side management—A simulation of household behavior under variable prices , 2011 .

[29]  J. Friedman.  Greedy function approximation: A gradient boosting machine. , 2001 .

[30]  N. Altman.  An Introduction to Kernel and Nearest-Neighbor Nonparametric Regression , 1992 .

[31]  Jean-Yves Fourniols,et al.  Wearable multi-sensor system for embedded body position and motion analysis during cycling View publication stats View publication stats , 2014 .

[32]  Wolfgang Ketter,et al.  Machine Learning Algorithms for Smart Electricity Markets , 2013 .