Abstract
The paper deals with the prediction of electricity demand, using data from smart meters obtained in defined time steps. We propose the modification of ensemble learning method called Dynamic Weighted Majority (DWM). The data are represented by data streams. According to our experiments, the proposed solution offers favorable alternative to current solutions. We also focus on the comparison of proposed ensemble method with single predictions used in the model.
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Acknowledgements
This work was partially supported by the Research and Development Operational Programme for the project “International Centre of Excellence for Research of Intelligent and Secure Information-Communication Technologies and Systems”, ITMS 26240120039, co-funded by the ERDF and the Scientific Grant Agency of The Slovak Republic, grant No. VG 1/0752/14 and VG 1/0646/15. The authors would also like to thank for financial contribution from the STU Grant scheme for Support of Young Researchers.
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Nemec, R., Rozinajová, V., Lóderer, M. (2017). Prediction of Power Load Demand Using Modified Dynamic Weighted Majority Method. In: Świątek, J., Tomczak, J. (eds) Advances in Systems Science. ICSS 2016. Advances in Intelligent Systems and Computing, vol 539. Springer, Cham. https://doi.org/10.1007/978-3-319-48944-5_4
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DOI: https://doi.org/10.1007/978-3-319-48944-5_4
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