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Electricity price classification using extreme learning machines

  • Extreme learning machine and applications
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Abstract

Forecasting electricity prices has been a widely investigated research issue in the deregulated power market scenario. High price volatilities, price spikes caused by a number of factors such as weather uncertainty, fluctuating fuel prices, transmission bottlenecks, etc., make the task of accurate price forecasting a formidable challenge for the market participants. A number of models have been proposed by researchers; however, achieving high accuracy is always not possible. In some specific applications such as self-scheduling by demand side participants, certain price thresholds are more useful than accurate price forecasts. In this paper, we have investigated the application of a novel neural network-based technique called extreme learning machine for the problem of classification of future electricity prices with respect to certain price thresholds. Different models corresponding to different lead times are developed and tested with data corresponding to Ontario and PJM markets. It is observed that classification with ELM is fast, less sensitive to user defined parameters and easily implementable.

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Authors and Affiliations

  1. Indian Institute of Technology Delhi, Hauz Khas, New Delhi, 110016, India

    Nitin Anand Shrivastava & Bijaya Ketan Panigrahi

  2. School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, Singapore, 639798, Singapore

    Meng-Hiot Lim

Authors
  1. Nitin Anand Shrivastava
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  2. Bijaya Ketan Panigrahi
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  3. Meng-Hiot Lim
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Correspondence to Nitin Anand Shrivastava.

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Shrivastava, N.A., Panigrahi, B.K. & Lim, MH. Electricity price classification using extreme learning machines. Neural Comput & Applic 27, 9–18 (2016). https://doi.org/10.1007/s00521-013-1537-1

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  • DOI: https://doi.org/10.1007/s00521-013-1537-1

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