Forecasting Market Clearing Prices in Electricity Markets with Time Series Based Machine Learning Models

Mehmet Bora Yağmur*, Kağan Turhan, Tolga Kaya

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The Turkish Electricity Market has experienced various procedural transformations over time. These changes have led to the establishment of a system in the electricity market that allows stakeholders to secure hourly energy through next-day sales and purchases. This system is known as the pre-day market and the price set within this framework is referred to as the market clearing price. This study was designed to predict the electricity price for the next 24 time units within the next 24 h in Turkey. Predictions of the market clearing price were conducted using numerous machine learning models. Time series data of clearing prices in Turkey were used in the analysis. Exogenous variables such as production amount and holiday dummies were also incorporated. The data period was from January 2021 to December 2023. The study utilized two lagged market clearing price features with 19 independent lagged and unlagged additional variables. Various machine learning models were tested for their efficacy in forecasting the market clearing price, to identify the most effective one. To benefit from the various advantages of different models, the three models with the best performance, lightGBM, OMP, and STLF were blended to obtain a new model.

Original languageEnglish
Title of host publicationIntelligent and Fuzzy Systems - Intelligent Industrial Informatics and Efficient Networks Proceedings of the INFUS 2024 Conference
EditorsCengiz Kahraman, Sezi Cevik Onar, Selcuk Cebi, Basar Oztaysi, Irem Ucal Sari, A. Cagrı Tolga
PublisherSpringer Science and Business Media Deutschland GmbH
Pages20-28
Number of pages9
ISBN (Print)9783031671913
DOIs
Publication statusPublished - 2024
EventInternational Conference on Intelligent and Fuzzy Systems, INFUS 2024 - Canakkale, Turkey
Duration: 16 Jul 202418 Jul 2024

Publication series

NameLecture Notes in Networks and Systems
Volume1090 LNNS
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

Conference

ConferenceInternational Conference on Intelligent and Fuzzy Systems, INFUS 2024
Country/TerritoryTurkey
CityCanakkale
Period16/07/2418/07/24

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Keywords

  • Electricity markets
  • electricity price
  • forecasting
  • lightGBM
  • machine learning
  • market clearing price

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