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Forecasting Electricity Demand by Hybrid Machine Learning Model

  • Shu Fan
  • Chengxiong Mao
  • Jiadong Zhang
  • Luonan Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4233)

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.

Keywords

Support Vector Machine Mean Absolute Percentage Error Marginal Likelihood Electricity Demand Load Forecast 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Shu Fan
    • 1
  • Chengxiong Mao
    • 2
  • Jiadong Zhang
    • 1
  • Luonan Chen
    • 1
  1. 1.Osaka Sangyo UniversityDaito, OsakaJapan
  2. 2.Huazhong University of Science and TechnologyWuhanChina

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