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Short-term smart learning electrical load prediction algorithm for home energy management systems

Author

Listed:
  • El-Baz, Wessam
  • Tzscheutschler, Peter

Abstract

Energy management system (EMS) within buildings has always been one of the main approaches for an automated demand side management (DSM). These energy management systems are supposed to increase load flexibility to fit more the generation from renewable energies and micro co-generation devices. For EMS to operate efficiently, it must learn ahead about the available supply and demand so that it can work on supply–demand matching and minimizing the imports from the grid and running costs. This article presents a simple efficient day-ahead electrical load prediction approach for any EMS. In comparison to other approaches, the presented algorithm was designed to be apart of any generic EMS and it does not require to be associated with a prepared statistical or historical databases, or even to get connected to any kinds of sensors. The proposed algorithm was tested over the data of 25 households in Austria and the results have shown an error range that goes down to 8.2% as an initial prediction.

Suggested Citation

  • El-Baz, Wessam & Tzscheutschler, Peter, 2015. "Short-term smart learning electrical load prediction algorithm for home energy management systems," Applied Energy, Elsevier, vol. 147(C), pages 10-19.
  • Handle: RePEc:eee:appene:v:147:y:2015:i:c:p:10-19
    DOI: 10.1016/j.apenergy.2015.01.122
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Baldi, Simone & Yuan, Shuai & Endel, Petr & Holub, Ondrej, 2016. "Dual estimation: Constructing building energy models from data sampled at low rate," Applied Energy, Elsevier, vol. 169(C), pages 81-92.
    2. Murray, D.M. & Liao, J. & Stankovic, L. & Stankovic, V., 2016. "Understanding usage patterns of electric kettle and energy saving potential," Applied Energy, Elsevier, vol. 171(C), pages 231-242.
    3. repec:eee:appene:v:212:y:2018:i:c:p:607-621 is not listed on IDEAS
    4. Nesamalar, J. Jeslin Drusila & Venkatesh, P. & Raja, S. Charles, 2016. "Energy management by generator rescheduling in congestive deregulated power system," Applied Energy, Elsevier, vol. 171(C), pages 357-371.
    5. repec:gam:jeners:v:10:y:2017:i:12:p:2107-:d:122453 is not listed on IDEAS
    6. Ogunjuyigbe, A.S.O. & Ayodele, T.R. & Akinola, O.A., 2017. "User satisfaction-induced demand side load management in residential buildings with user budget constraint," Applied Energy, Elsevier, vol. 187(C), pages 352-366.
    7. repec:gam:jeners:v:10:y:2017:i:9:p:1258-:d:109673 is not listed on IDEAS
    8. repec:eee:appene:v:212:y:2018:i:c:p:997-1012 is not listed on IDEAS
    9. repec:eee:appene:v:236:y:2019:i:c:p:273-292 is not listed on IDEAS
    10. repec:eee:energy:v:167:y:2019:i:c:p:511-522 is not listed on IDEAS

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