A class-driven approach based on long short-term memory networks for electricity price scenario generation and reduction

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Abstract

Uncertainty characterization is an essential component of decision-making problems in electricity markets. In this work, a class-driven approach is proposed to describe stochasticity. The methodology consists of a three-step process that includes a class allocation component, a generative element based on a long short-term memory neural network and an automated reduction method with a variance-based continuation criterion. The system is employed and evaluated on Dutch imbalance market prices. Test results are presented, expressing the proficiency of the approach, both in generating realistic scenario sets that reflect the erratic dynamics in the data and adequately reducing generated sets without the need to explicitly and manually predetermine the cardinality of the reduced set.
Original languageEnglish
JournalIEEE Transactions on Power Systems
DOIs
Publication statusAccepted/In press - 13 Jan 2020

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Keywords

  • Artificial intelligence
  • Deep learning
  • Long short-term memory (lstm)
  • Recurrent neural networks (rnn)
  • Uncertainty
  • Machine learning
  • Scenario generation
  • Scenario reduction
  • Imbalance prices

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