A Class-Driven Approach Based on Long Short-Term Memory Networks for Electricity Price Scenario Generation and Reduction

Bart Stappers, N.G. Paterakis (Corresponding author), J.K. (Koen) Kok, M. Gibescu

Research output: Contribution to journalArticleAcademicpeer-review

1 Citation (Scopus)
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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
Article number8957258
Pages (from-to)3040-3050
Number of pages11
JournalIEEE Transactions on Power Systems
Volume35
Issue number4
DOIs
Publication statusPublished - Jul 2020

Keywords

  • Deep learning
  • imbalance prices
  • long short-term memory (LSTM)
  • machine learning
  • recurrent neural network (RNN)
  • scenario generation
  • scenario reduction

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