Skip to main content

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 293))

  • 1189 Accesses

Abstract

This paper reviews previous and recent trends in energy management systems (EMS) and energy information communication technologies (EICT) for smart home applications. Relevant EMS and EICT publications on smart homes are reviewed. This paper first analyzes different energy management approaches for smart home applications, including fuzzy logic, neural networks, heuristic methods, and evolution-based approaches. Then, various EICT approaches are surveyed to evaluate the feasibility of smart home applications by discussing historical developments and introducing advanced EICT methods. Importantly, this paper contributes to efforts to further advanced energy management technologies for smart home applications.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 329.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Leeds, D. J. (2009). The smart grid in 2010: market segments, applications and industry players. Greentech Media Inc.

    Google Scholar 

  2. Lien, C. H., Bai, Y. W., & Lin, M. B. (2007). Remote-controllable power outlet system for home power management. IEEE Transactions on Consumer Electronics, 53, 1634–1641.

    Article  Google Scholar 

  3. Roy, A., Das, S., & Basu, K. (2007). A predictive framework for location-aware resource management in smart homes. IEEE Transaction on Mobile Computer, 6, 1270–1283.

    Article  Google Scholar 

  4. Zhang, L., Leung, H., & Chan, K. (2008). Information fusion based smart home control system and its application. IEEE Transactions on Consumer Electronics, 54, 1157–1165.

    Article  Google Scholar 

  5. Vainio, A. M., Valtonen, M., & Vanhala, J. (2008). Proactive fuzzy control and adaptation methods for smart homes. IEEE Intelligent Systems, 23, 42–49.

    Article  Google Scholar 

  6. Zhao, J. H., Dong, Z. Y., Xu, Z., & Wong, K. P. (2008). A statistical approach for interval forecasting of the electricity price. IEEE Transactions on Power Systems, 23, 267–276.

    Article  Google Scholar 

  7. Pedrasa, M. A. Spooner, E. D., & MacGill, I. F. (2009). Improved energy services provision through the intelligent control of distributed energy resources. In 2009 IEEE Bucharest Power Tech Conference, pp. 1–8.

    Google Scholar 

  8. Han, D., & Lim, J. (2010). Smart home energy management system using IEEE 802.15.4 and ZigBee. IEEE Transactios on Consumer Electronics, 56, 1403–1410.

    Google Scholar 

  9. Pedrasa, M. A. A., Spooner, T. D., & MacGill, I. F. (2010). Coordinated scheduling of residential distributed energy resources to optimize smart home energy services. IEEE Transactions on Smart Grid, 1, 134–143.

    Article  Google Scholar 

  10. Mohsenian-Rad, A. H., & Leon-Garcia, A. (2010). Optimal residential load control with price prediction in real-time electricity pricing environments. IEEE Transactions on Smart Grid, 1, 120–133.

    Article  Google Scholar 

  11. Du, P., & Lu, N. (2011). Appliance commitment for household load scheduling. IEEE Transactions on Smart Grid, 2, 411–419.

    Article  Google Scholar 

  12. Motamedi, A., Zareipour, H., & Rosehart, W. D. (2012). Electricity price and demand forecasting in smart grids. IEEE Transactions on Smart Grid, 3, 664–674.

    Article  Google Scholar 

  13. Huang, C. M., Yang, S. P., Yang, H. T., & Huang, Y. C. (2012). Combined particle swarm optimization and heuristic fuzzy inference systems for a smart home one-step-ahead load forecasting. Journal of the Chinese Institute of Engineers, 35, 1–10.

    Article  Google Scholar 

Download references

Acknowledgments

Financial supports from the National Science Council, Taiwan, R.O.C. under the Grant No. NSC 102-3113-P-006-015 and NSC 101-2632-E-230-001-MY3 are acknowledged.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yann-Chang Huang .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer International Publishing Switzerland

About this paper

Cite this paper

Sun, HC., Huang, YC., Huang, CM., Tung, CC. (2014). Energy Management Technologies for Smart Home Applications. In: Juang, J., Chen, CY., Yang, CF. (eds) Proceedings of the 2nd International Conference on Intelligent Technologies and Engineering Systems (ICITES2013). Lecture Notes in Electrical Engineering, vol 293. Springer, Cham. https://doi.org/10.1007/978-3-319-04573-3_82

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-04573-3_82

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-04572-6

  • Online ISBN: 978-3-319-04573-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics