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SmartCity 360 2016, SmartCity 360 2015: Smart City 360° pp 431-442 | Cite as

Day-Ahead Electricity Spike Price Forecasting Using a Hybrid Neural Network-Based Method

  • Harmanjot Singh Sandhu
  • Liping Fang
  • Ling Guan
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 166)

Abstract

A hybrid neural network-based method is presented to predict day-ahead electricity spike prices in a deregulated electricity market. First, prediction of day-ahead electricity prices is carried out by a neural network along with pre-processing data mining techniques. Second, a classifier is used to separate the forecasted prices into normal and spike prices. Third, a second neural network is trained over spike hours with selected features and is used to forecast day-ahead spike prices. Forecasted spike and normal prices are combined to produce the complete day-ahead hourly electricity price forecasting. Numerical experiments demonstrate that the proposed method can significantly improve the forecasting accuracy.

Keywords

Neural network Price spikes Day-ahead forecasting Electricity market 

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2016

Authors and Affiliations

  • Harmanjot Singh Sandhu
    • 1
  • Liping Fang
    • 1
  • Ling Guan
    • 2
  1. 1.Department of Mechanical and Industrial EngineeringRyerson UniversityTorontoCanada
  2. 2.Department of Electrical and Computer EngineeringRyerson UniversityTorontoCanada

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