research-article

Energy disaggregation meets heating control

Authors:

Stephan Spiegel,

Sahin AlbayrakAuthors Info & Claims

SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied Computing

Pages 559 - 566

https://doi.org/10.1145/2554850.2555088

Published: 24 March 2014 Publication History

Abstract

Heating control is of particular importance, since heating accounts for the biggest amount of total residential energy consumption. Smart heating strategies allow to reduce such energy consumption by automatically turning off the heating when the occupants are sleeping or away from home. The present context or occupancy state of a household can be deduced from the appliances that are currently in use. In this study we investigate energy disaggregation techniques to infer appliance states from an aggregated energy signal measured by a smart meter. Since most household devices have predictable energy consumption, we propose to use the changes in aggregated energy consumption as features for the appliance/occupancy state classification task. We evaluate our approach on real-life energy consumption data from several households, compare the classification accuracy of various machine learning techniques, and explain how to use the inferred appliance states to optimize heating schedules.

References

[1]

MathWorks (TM), (R2013a). Statistics Toolbox: Supervised Learning Workflow and Algorithms.

[2]

K. C. Armel, A. Gupta, G. Shrimali, and A. Albert. Is disaggregation the holy grail of energy efficiency? the case of electricity. Energy Policy, 52(C): 213--234, 2013.

[3]

C. Beckel, W. Kleiminger, T. Staake, and S. Santini. Improving device-level electricity consumption breakdowns in private households using on/off events. SIGBED Rev., 9(3): 32--38, 2012.

Digital Library

[4]

P. Boait and R. Rylatt. A method for fully automatic operation of domestic heating. Energy and Buildings, 42(1): 11--16, 2010.

[5]

J. Froehlich, E. Larson, S. Gupta, G. Cohn, M. Reynolds, and S. Patel. Disaggregated end-use energy sensing for the smart grid. IEEE Pervasive Computing, 10(1): 28--39, 2011.

Digital Library

[6]

Z. Ghahramani and M. I. Jordan. Factorial hidden markov models. Machine Learning, 29(2--3): 245--273, 1997.

Digital Library

[7]

S. Gupta, M. S. Reynolds, and S. N. Patel. Electrisense: single-point sensing using emi for electrical event detection and classification in the home. In Proceedings of the 12th ACM international conference on Ubiquitous computing, pages 139--148, 2010.

Digital Library

[8]

E. Keogh and S. Kasetty. On the need for time series data mining benchmarks: A survey and empirical demonstration. Data Min. Knowl. Discov., 7(4): 349--371, 2003.

Digital Library

[9]

H. Kim, M. Marwah, M. F. Arlitt, G. Lyon, and J. Han. Unsupervised disaggregation of low frequency power measurements. In SDM, pages 747--758, 2011.

[10]

W. Kleiminger, C. Beckel, and S. Santini. Opportunistic sensing for efficient energy usage in private households. In Proceedings of the Smart Energy Strategies Conference 2011, 2011.

[11]

J. Z. Kolter and M. J. Johnson. Redd: A public data set for energy disaggregation research. In Proc. of SustKDD Workshop on Data Mining Applications in Sustainability, 2011.

[12]

J. Liang, S. Ng, G. Kendall, and J. Cheng. Load signature study - part i: Basic concept, structure, and methodology. IEEE Trans. on Power Delivery, 25(2): 551--560, 2010.

[13]

G.-Y. Lin, S.-C. Lee, and J. Y.-J. Hsu. Sensing from the panel: Applying the power meters for appliance recognition. In Proceedings of the 14th Conference on Artificial Intelligence and Applications, 2009.

[14]

J. Lines, A. Bagnall, P. Caiger-Smith, and S. Anderson. Classification of household devices by electricity usage profiles. In Proc. of Int. Conf. on Intelligent Data Engineering and Automated Learning, pages 403--412, 2011.

Digital Library

[15]

J. Lu, T. Sookoor, V. Srinivasan, G. Gao, B. Holben, J. Stankovic, E. Field, and K. Whitehouse. The smart thermostat: using occupancy sensors to save energy in homes. In Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems, pages 211--224, 2010.

Digital Library

[16]

V. Pallotta, P. Bruegger, and B. Hirsbrunner. Smart heating systems: Optimizing heating systems by kinetic-awareness. In Proc. of 3rd International Conference on Digital Information Management, 2008.

[17]

A. Reinhardt, P. Baumann, D. Burgstahler, M. Hollick, H. Chonov, M. Werner, and R. Steinmetz. On the accuracy of appliance identification based on distributed load metering data. In Proc. of 2nd IFIP Conference on Sustainable Internet and ICT for Sustainability, 2012.

[18]

A. G. Ruzzelli, C. Nicolas, A. Schoofs, and G. M. P. O'Hare. Real-time recognition and profiling of appliances through a single electricity sensor. In Proceedings of the 7th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, pages 279--287, 2010.

[19]

K. Suzuki, S. Inagaki, T. Suzuki, H. Nakamura, and K. Ito. Nonintrusive appliance load monitoring based on integer programming. In Proc. on SICE Annual Conference, 2008.

[20]

M. Weiss, A. Helfenstein, F. Mattern, and T. Staake. Leveraging smart meter data to recognize home appliances. In Proc. of IEEE International Conference on Pervasive Computing and Communications, pages 190--197, 2012.

[21]

M. Zeifman and K. Roth. Nonintrusive appliance load monitoring: Review and outlook. IEEE Trans. Consumer Electronics, 57(1): 76--84, 2011.

Digital Library

[22]

A. Zoha, A. Gluhak, M. A. Imran, and S. Rajasegarar. Non-intrusive load monitoring approaches for disaggregated energy sensing: A survey. Sensors, 12(12): 16838--16866, 2012.

Cited By

Yin BLi ZXu JLi LYang XDu Z(2021)Non-intrusive load monitoring algorithm based on household electricity use habitsNeural Computing and Applications10.1007/s00521-021-06088-234:18(15273-15291)Online publication date: 21-May-2021
https://doi.org/10.1007/s00521-021-06088-2
Kuijer LGiaccardi EMandryk RHancock MPerry MCox A(2018)Co-performanceProceedings of the 2018 CHI Conference on Human Factors in Computing Systems10.1145/3173574.3173699(1-13)Online publication date: 21-Apr-2018
https://dl.acm.org/doi/10.1145/3173574.3173699
Yang CSoh CYap V(2017)A systematic approach in appliance disaggregation using k-nearest neighbours and naive Bayes classifiers for energy efficiencyEnergy Efficiency10.1007/s12053-017-9561-011:1(239-259)Online publication date: 21-Aug-2017
https://doi.org/10.1007/s12053-017-9561-0
Show More Cited By

Index Terms

Energy disaggregation meets heating control
1. Computing methodologies
  1. Machine learning
2. Mathematics of computing
  1. Probability and statistics

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Published In

cover image ACM Conferences

SAC '14: Proceedings of the 29th Annual ACM Symposium on Applied Computing

March 2014

1890 pages

ISBN:9781450324694

DOI:10.1145/2554850

Conference Chairs:
Yookun Cho
Seoul National University, Korea
,
Sung Y. Shin
South Dakota State University
,
Program Chairs:
Sangwook Kim
Kyungpook National University, Korea
,
Chih-Cheng Hung
Southern Polytechnic State University
,
Jiman Hong
Soongsil University, South Korea

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 March 2014

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SAC 2014

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SIGAPP

SAC 2014: Symposium on Applied Computing

March 24 - 28, 2014

Gyeongju, Republic of Korea

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SAC '14 Paper Acceptance Rate 218 of 939 submissions, 23%;

Overall Acceptance Rate 1,650 of 6,669 submissions, 25%

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The 40th ACM/SIGAPP Symposium on Applied Computing

March 31 - April 4, 2025

Catania , Italy

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Cited By

Yin BLi ZXu JLi LYang XDu Z(2021)Non-intrusive load monitoring algorithm based on household electricity use habitsNeural Computing and Applications10.1007/s00521-021-06088-234:18(15273-15291)Online publication date: 21-May-2021
https://doi.org/10.1007/s00521-021-06088-2
Kuijer LGiaccardi EMandryk RHancock MPerry MCox A(2018)Co-performanceProceedings of the 2018 CHI Conference on Human Factors in Computing Systems10.1145/3173574.3173699(1-13)Online publication date: 21-Apr-2018
https://dl.acm.org/doi/10.1145/3173574.3173699
Yang CSoh CYap V(2017)A systematic approach in appliance disaggregation using k-nearest neighbours and naive Bayes classifiers for energy efficiencyEnergy Efficiency10.1007/s12053-017-9561-011:1(239-259)Online publication date: 21-Aug-2017
https://doi.org/10.1007/s12053-017-9561-0
Lützenberger MMasuch NKüster TFreund DVoß MHrabia CPozo DFähndrich JTrollmann FKeiser JAlbayrak S(2015)A common approach to intelligent energy and mobility services in a smart city environmentJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-015-0263-16:3(337-350)Online publication date: 21-Mar-2015
https://doi.org/10.1007/s12652-015-0263-1
Beckel CKleiminger WCicchetti RStaake TSantini SSrivastava M(2014)The ECO data set and the performance of non-intrusive load monitoring algorithmsProceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings10.1145/2674061.2674064(80-89)Online publication date: 3-Nov-2014
https://dl.acm.org/doi/10.1145/2674061.2674064

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