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

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

Samenvatting

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.
Originele taal-2Engels
Pagina's (van-tot)3040 - 3050
TijdschriftIEEE Transactions on Power Systems
Volume35
Nummer van het tijdschrift4
DOI's
StatusGepubliceerd - 1 jul 2020

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