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


  • El-Baz, Wessam
  • Tzscheutschler, Peter


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

    1. Di Giorgio, Alessandro & Liberati, Francesco, 2014. "Near real time load shifting control for residential electricity prosumers under designed and market indexed pricing models," Applied Energy, Elsevier, vol. 128(C), pages 119-132.
    2. Tzafestas, S.G. & Dalianis, P.J. & Anthopoulos, G., 1996. "On the overtraining phenomenon of backpropagation neural networks," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 40(5), pages 507-521.
    3. Cassola, Federico & Burlando, Massimiliano, 2012. "Wind speed and wind energy forecast through Kalman filtering of Numerical Weather Prediction model output," Applied Energy, Elsevier, vol. 99(C), pages 154-166.
    4. Andersen, F.M. & Larsen, H.V. & Gaardestrup, R.B., 2013. "Long term forecasting of hourly electricity consumption in local areas in Denmark," Applied Energy, Elsevier, vol. 110(C), pages 147-162.
    5. Warren, Peter, 2014. "A review of demand-side management policy in the UK," Renewable and Sustainable Energy Reviews, Elsevier, vol. 29(C), pages 941-951.
    6. McPherson, Madeleine & Karney, Bryan, 2014. "Long-term scenario alternatives and their implications: LEAP model application of Panama׳s electricity sector," Energy Policy, Elsevier, vol. 68(C), pages 146-157.
    7. Gottwalt, Sebastian & Ketter, Wolfgang & Block, Carsten & Collins, John & Weinhardt, Christof, 2011. "Demand side management—A simulation of household behavior under variable prices," Energy Policy, Elsevier, vol. 39(12), pages 8163-8174.
    8. Pappas, S.Sp. & Ekonomou, L. & Karamousantas, D.Ch. & Chatzarakis, G.E. & Katsikas, S.K. & Liatsis, P., 2008. "Electricity demand loads modeling using AutoRegressive Moving Average (ARMA) models," Energy, Elsevier, vol. 33(9), pages 1353-1360.
    9. Di Giorgio, Alessandro & Pimpinella, Laura, 2012. "An event driven Smart Home Controller enabling consumer economic saving and automated Demand Side Management," Applied Energy, Elsevier, vol. 96(C), pages 92-103.
    10. Zúñiga, K.V. & Castilla, I. & Aguilar, R.M., 2014. "Using fuzzy logic to model the behavior of residential electrical utility customers," Applied Energy, Elsevier, vol. 115(C), pages 384-393.
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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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