Abstract
Focused on Load forecasting for electric power plan, a novel prediction model, which was based on machine learning, was established. We propose Bagging algorithm optimized Extreme Learning Machine (ELM) prediction model with the fast learning ability of ELM and weight altering of Bagging to increase the prediction accuracy. Finally, it is applied on short term load forecasting problem verified by the EUNITE load forecasting datasets. Compared with winning algorithm of EUNITE competition, Bagging-ELM prediction model has a better performance on prediction accuracy.
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Xu, Rz., Geng, Xf., Zhou, Fy. (2013). A Short Term Load Forecasting Based on Bagging-ELM Algorithm. In: Lu, W., Cai, G., Liu, W., Xing, W. (eds) Proceedings of the 2012 International Conference on Information Technology and Software Engineering. Lecture Notes in Electrical Engineering, vol 211. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34522-7_54
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DOI: https://doi.org/10.1007/978-3-642-34522-7_54
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