Abstract
This paper proposes a hybrid machine learning model for electricity demand forecasting, based on Bayesian Clustering by Dynamics (BCD) and Support Vector Machine (SVM). In the proposed model, a BCD classifier is firstly applied to cluster the input data set into several subsets by the dynamics of load series in an unsupervised manner, and then, groups of 24 SVMs for the next day’s electricity demand curve are used to fit the training data of each subset. In the numerical experiment, the proposed model has been trained and tested on the data of the historical load from New York City.
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Fan, S., Mao, C., Zhang, J., Chen, L. (2006). Forecasting Electricity Demand by Hybrid Machine Learning Model. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4233. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893257_105
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DOI: https://doi.org/10.1007/11893257_105
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46481-5
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