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Electricity Load Forecasting: A Weekday-Based Approach

  • Irena Koprinska
  • Mashud Rana
  • Vassilios G. Agelidis
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7553)

Abstract

We present a new approach for building weekday-based prediction models for electricity load forecasting. The key idea is to conduct a local feature selection using autocorrelation analysis for each day of the week and build a separate prediction model using linear regression and backpropagation neural networks. We used two years of 5-minute electricity load data for the state of New South Wales in Australia to evaluate performance. Our results showed that the weekday-based local prediction model, when used with linear regression, obtained a small and statistically significant increase in accuracy in comparison with the global (one for all days) prediction model. Both models, local and global, when used with linear regression were accurate and fast to train and are suitable for practical applications.

Keywords

electricity load forecasting autocorrelation analysis linear regression backpropagation neural networks weekday-based prediction model 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Irena Koprinska
    • 1
  • Mashud Rana
    • 1
  • Vassilios G. Agelidis
    • 2
  1. 1.School of Information TechnologiesUniversity of SydneySydneyAustralia
  2. 2.Australian Energy Research InstituteUniversity of New South WalesSydneyAustralia

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