Skip to main content
SpringerLink
Log in
Book cover

EPIA Conference on Artificial Intelligence

EPIA 2021: Progress in Artificial Intelligence pp 151–162Cite as

Metaheuristics for Optimal Scheduling of Appliances in Energy Efficient Neighbourhoods

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12981))

Abstract

As a consequence of the continuous growth in the worldwide electricity consumption, supplying all customer electrical requests is becoming increasingly difficult for electricity companies. That is why, they encourage their clients to actively manage their own demand, providing several resources such us their Optimal Demand Profile (ODP). This profile provides to users a summary of the demand they should consume during the day. However, this profile needs to be translated into specific control actions first, such as the when each appliance should be used. In this article a comparison of the performance of two metaheuristic optimisation algorithms (Tabu Search and Estimation of Distribution Algorithm (EDA)) and their variants for the calculation of optimal appliance scheduling is presented. Results show that Tabu Search algorithm can reach better feasible solutions at faster execution times than EDA does.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   109.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   139.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Learn about institutional subscriptions

Notes

  1. 1.

    https://www.iea.org/.

  2. 2.

    http://project-respond.eu/.

References

  1. Ata, M., Erenoğlu, A.K., Şengör, İ, Erdinç, O., Taşcıkaraoğlu, A., Catalão, J.P.: Optimal operation of a multi-energy system considering renewable energy sources stochasticity and impacts of electric vehicles. Energy 186, 115841 (2019)

    Article  Google Scholar 

  2. Barbato, A., Capone, A., Carello, G., Delfanti, M., Merlo, M., Zaminga, A.: House energy demand optimization in single and multi-user scenarios. In: 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm), pp. 345–350. IEEE (2011)

    Google Scholar 

  3. Beiranvand, V., Hare, W., Lucet, Y.: Best practices for comparing optimization algorithms. Optim. Eng. 18(4), 815–848 (2017). https://doi.org/10.1007/s11081-017-9366-1

    Article  MathSciNet  MATH  Google Scholar 

  4. Bouakkaz, A., Haddad, S., Gil Mena, A.J.: Optimal peak power shaving through household appliance scheduling in off-grid renewable energy system. In: 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC/I CPS Europe), pp. 1–6 (2019). https://doi.org/10.1109/EEEIC.2019.8783662

  5. Chavali, P., Yang, P., Nehorai, A.: A distributed algorithm of appliance scheduling for home energy management system. IEEE Trans. Smart Grid 5(1), 282–290 (2014)

    Article  Google Scholar 

  6. Dorahaki, S., Dashti, R., Shaker, H.R.: Optimal energy management in the smart microgrid considering the electrical energy storage system and the demand-side energy efficiency program. J. Energy Storage 28, 101229 (2020)

    Article  Google Scholar 

  7. Esnaola-Gonzalez, I., Jelić, M., Pujić, D., Díez, F., Tomasevic, N.: An AI-powered system for residential demand response. Electronics 10 (2021). https://doi.org/10.3390/electronics10060693

  8. Gould, N., Scott, J.: A note on performance profiles for benchmarking software. ACM Trans. Math. Softw. (TOMS) 43(2), 1–5 (2016)

    Article  MathSciNet  Google Scholar 

  9. Huang, L., Walrand, J., Ramchandran, K.: Optimal demand response with energy storage management. In: 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm), pp. 61–66. IEEE (2012)

    Google Scholar 

  10. Jamil, A., Javaid, N., Aslam, S.: An efficient home energy optimization by using meta-heuristic techniques while incorporating game-theoretic approach for real-time coordination among home appliances. In: 2018 5th International Multi-Topic ICT Conference (IMTIC), pp. 1–6 (2018). https://doi.org/10.1109/IMTIC.2018.8467218

  11. Lorestani, A., Aghaee, S.S., Gharehpetian, G.B., Ardehali, M.M.: Energy management in smart home including PV panel, battery, electric heater with integration of plug-in electric vehicle. In: 2017 Smart Grid Conference (SGC), pp. 1–7 (2017). https://doi.org/10.1109/SGC.2017.8308855

  12. Mirabbasi, D., Beydaghi, S.: Optimal scheduling of smart home appliances considering PHEV and energy storage system. In: 2015 4th International Conference on Electric Power and Energy Conversion Systems (EPECS), pp. 1–6 (2015). https://doi.org/10.1109/EPECS.2015.7368510

  13. Nan, S., Zhou, M., Li, G.: Optimal residential community demand response scheduling in smart grid. Appl. Energy 210, 1280–1289 (2018)

    Article  Google Scholar 

  14. Yu, Y., Wang, W., Kuang, H.: A two-stage scheduling on household appliances including electric vehicles. In: 2016 3rd International Conference on Systems and Informatics (ICSAI), pp. 258–262 (2016). https://doi.org/10.1109/ICSAI.2016.7810964

Download references

Acknowledgements

This work was supported by the SPRI-Basque Government’s project 3KIA [grant number KK-2020/00049] of the ELKARTEK program.

Author information

Authors and Affiliations

  1. TEKNIKER, Basque Research and Technology Alliance (BRTA), C/Iñaki Goenaga 5, 20600, Eibar, Spain

    Amaia Alfageme, Iker Esnaola-Gonzalez, Francisco Javier Díez & Eduardo Gilabert

  2. Faculty of Informatics, University of the Basque Country (UPV/EHU), Paseo Manuel Lardizabal 1, 20018, Donostia-San Sebastián, Spain

    Amaia Alfageme

Authors
  1. Amaia Alfageme
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Iker Esnaola-Gonzalez
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Francisco Javier Díez
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Eduardo Gilabert
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Iker Esnaola-Gonzalez .

Editor information

Editors and Affiliations

  1. ISEP/GECAD, Polytechnic Institute of Porto, Porto, Portugal

    Goreti Marreiros

  2. IST/INESC-ID, University of Lisbon, Porto Salvo, Portugal

    Francisco S. Melo

  3. DETI/IEETA, University of Aveiro, Aveiro, Portugal

    Nuno Lau

  4. FEUP/LIACC, University of Porto, Porto, Portugal

    Henrique Lopes Cardoso

  5. FEUP/LIACC, University of Porto, Porto, Portugal

    Luís Paulo Reis

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Alfageme, A., Esnaola-Gonzalez, I., Díez, F.J., Gilabert, E. (2021). Metaheuristics for Optimal Scheduling of Appliances in Energy Efficient Neighbourhoods. In: Marreiros, G., Melo, F.S., Lau, N., Lopes Cardoso, H., Reis, L.P. (eds) Progress in Artificial Intelligence. EPIA 2021. Lecture Notes in Computer Science(), vol 12981. Springer, Cham. https://doi.org/10.1007/978-3-030-86230-5_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-86230-5_12

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86229-9

  • Online ISBN: 978-3-030-86230-5

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation