IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v63y2024i6d10.1007_s10614-023-10416-0.html
   My bibliography  Save this article

From the East-European Regional Day-Ahead Markets to a Global Electricity Market

Author

Listed:
  • Adela Bâra

    (Bucharest University of Economic Studies)

  • Simona-Vasilica Oprea

    (Bucharest University of Economic Studies)

  • Bogdan George Tudorică

    (Petroleum-Gas University of Ploiești)

Abstract

The so-called black swans, COVID-19 and the invasion in Ukraine, have led to an unprecedented increase in electricity prices. Since 2021, after lockdowns, the electricity price has started to increase due to economic recovery, rising prices of the tCO2 and other primary sources that become unavailable or at much higher prices. In this context, we noticed that the variation of electricity prices in one country can be explained by the price fluctuations of the previous day in the neighboring countries. For instance, the prices for the current day (d) in the Romanian Day-Ahead Market is strongly correlated with the prices of the previous day (d−1) on DAMs of its neighboring countries. It is worth mentioning that the target can be switched by the rest of the variables. Not only the price in Romania can be estimated using the proposed Electricity Price Forecast (EPF) method, but also the prices in other neighboring countries can be a target for prediction because the regional prices on similar markets contain most of society’s distress. Another interesting aspect is that the proposed forecasting methodology is robust, as proved by testing it on a varied and longer time interval (from January 2019 to August 2022). Furthermore, the proposed price forecasting methodology includes the adjustment of training interval according to the price standard deviation and weighing the results of the five individual Machine Learning (ML) algorithms to further improve the prediction performance. The set consists of data collected between 1st of January 2019—one year before COVID-19 pandemic outburst and 31st of August—several months after the war has started in the Black Sea region.

Suggested Citation

  • Adela Bâra & Simona-Vasilica Oprea & Bogdan George Tudorică, 2024. "From the East-European Regional Day-Ahead Markets to a Global Electricity Market," Computational Economics, Springer;Society for Computational Economics, vol. 63(6), pages 2525-2557, June.
    • Handle: RePEc:kap:compec:v:63:y:2024:i:6:d:10.1007_s10614-023-10416-0
    DOI: 10.1007/s10614-023-10416-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-023-10416-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10614-023-10416-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Simon Schnürch & Andreas Wagner, 2020. "Electricity Price Forecasting with Neural Networks on EPEX Order Books," Applied Mathematical Finance, Taylor & Francis Journals, vol. 27(3), pages 189-206, May.
    2. Florian Ziel & Rick Steinert, 2015. "Electricity Price Forecasting using Sale and Purchase Curves: The X-Model," Papers 1509.00372, arXiv.org, revised Aug 2016.
    3. Lu, Xin & Qiu, Jing & Lei, Gang & Zhu, Jianguo, 2022. "Scenarios modelling for forecasting day-ahead electricity prices: Case studies in Australia," Applied Energy, Elsevier, vol. 308(C).
    4. Per B. Solibakke, 2022. "Step‐ahead spot price densities using daily synchronously reported prices and wind forecasts," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 17-42, January.
    5. Helen Thompson, 2022. "The geopolitics of fossil fuels and renewables reshape the world," Nature, Nature, vol. 603(7901), pages 364-364, March.
    6. Bruno Bernal & Juan Carlos Molero & Fernando Perez De Gracia, 2019. "Impact of fossil fuel prices on electricity prices in Mexico," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 46(2), pages 356-371, March.
    7. Li, Wei & Becker, Denis Mike, 2021. "Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling," Energy, Elsevier, vol. 237(C).
    8. Tschora, Léonard & Pierre, Erwan & Plantevit, Marc & Robardet, Céline, 2022. "Electricity price forecasting on the day-ahead market using machine learning," Applied Energy, Elsevier, vol. 313(C).
    9. Massaoudi, Mohamed & Refaat, Shady S. & Chihi, Ines & Trabelsi, Mohamed & Oueslati, Fakhreddine S. & Abu-Rub, Haitham, 2021. "A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Forecasting," Energy, Elsevier, vol. 214(C).
    10. Spodniak, Petr & Ollikka, Kimmo & Honkapuro, Samuli, 2021. "The impact of wind power and electricity demand on the relevance of different short-term electricity markets: The Nordic case," Applied Energy, Elsevier, vol. 283(C).
    11. Liu, Luyao & Bai, Feifei & Su, Chenyu & Ma, Cuiping & Yan, Ruifeng & Li, Hailong & Sun, Qie & Wennersten, Ronald, 2022. "Forecasting the occurrence of extreme electricity prices using a multivariate logistic regression model," Energy, Elsevier, vol. 247(C).
    12. Léonard Tschora & Erwan Pierre & Marc Plantevit & Céline Robardet, 2022. "Electricity price forecasting on the day-ahead market using machine learning," Post-Print hal-03621974, HAL.
    13. Wei Li & Denis Mike Becker, 2021. "Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market coupling," Papers 2101.05249, arXiv.org, revised Jul 2021.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Daniel Manfre Jaimes & Manuel Zamudio López & Hamidreza Zareipour & Mike Quashie, 2023. "A Hybrid Model for Multi-Day-Ahead Electricity Price Forecasting considering Price Spikes," Forecasting, MDPI, vol. 5(3), pages 1-23, July.
    2. Madadkhani, Shiva & Ikonnikova, Svetlana, 2024. "Toward high-resolution projection of electricity prices: A machine learning approach to quantifying the effects of high fuel and CO2 prices," Energy Economics, Elsevier, vol. 129(C).
    3. van Zyl, Corne & Ye, Xianming & Naidoo, Raj, 2024. "Harnessing eXplainable artificial intelligence for feature selection in time series energy forecasting: A comparative analysis of Grad-CAM and SHAP," Applied Energy, Elsevier, vol. 353(PA).
    4. Deniz Kenan Kılıç & Peter Nielsen & Amila Thibbotuwawa, 2024. "Intraday Electricity Price Forecasting via LSTM and Trading Strategy for the Power Market: A Case Study of the West Denmark DK1 Grid Region," Energies, MDPI, vol. 17(12), pages 1-15, June.
    5. Meng, Anbo & Zhu, Jianbin & Yan, Baiping & Yin, Hao, 2024. "Day-ahead electricity price prediction in multi-price zones based on multi-view fusion spatio-temporal graph neural network," Applied Energy, Elsevier, vol. 369(C).
    6. Sergio Cantillo-Luna & Ricardo Moreno-Chuquen & Jesus Lopez-Sotelo & David Celeita, 2023. "An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture," Energies, MDPI, vol. 16(19), pages 1-24, September.
    7. Hakan Acaroğlu & Fausto Pedro García Márquez, 2021. "Comprehensive Review on Electricity Market Price and Load Forecasting Based on Wind Energy," Energies, MDPI, vol. 14(22), pages 1-23, November.
    8. Loizidis, Stylianos & Kyprianou, Andreas & Georghiou, George E., 2024. "Electricity market price forecasting using ELM and Bootstrap analysis: A case study of the German and Finnish Day-Ahead markets," Applied Energy, Elsevier, vol. 363(C).
    9. Wu, Han & Liang, Yan & Gao, Xiao-Zhi & Du, Pei, 2024. "Auditory-circuit-motivated deep network with application to short-term electricity price forecasting," Energy, Elsevier, vol. 288(C).
    10. Chai, Shanglei & Li, Qiang & Abedin, Mohammad Zoynul & Lucey, Brian M., 2024. "Forecasting electricity prices from the state-of-the-art modeling technology and the price determinant perspectives," Research in International Business and Finance, Elsevier, vol. 67(PA).
    11. Karol Pilot & Alicja Ganczarek-Gamrot & Krzysztof Kania, 2024. "Dealing with Anomalies in Day-Ahead Market Prediction Using Machine Learning Hybrid Model," Energies, MDPI, vol. 17(17), pages 1-20, September.
    12. Alola, Andrew Adewale & Özkan, Oktay & Usman, Ojonugwa, 2023. "Examining crude oil price outlook amidst substitute energy price and household energy expenditure in the USA: A novel nonparametric multivariate QQR approach," Energy Economics, Elsevier, vol. 120(C).
    13. Shi, Tao & Li, Chongyang & Zhang, Wei & Zhang, Yi, 2023. "Forecasting on metal resource spot settlement price: New evidence from the machine learning model," Resources Policy, Elsevier, vol. 81(C).
    14. Dounia El Bourakadi & Hiba Ramadan & Ali Yahyaouy & Jaouad Boumhidi, 2023. "A robust energy management approach in two-steps ahead using deep learning BiLSTM prediction model and type-2 fuzzy decision-making controller," Fuzzy Optimization and Decision Making, Springer, vol. 22(4), pages 645-667, December.
    15. Schneider, Nicolas & Strielkowski, Wadim, 2023. "Modelling the unit root properties of electricity data—A general note on time-domain applications," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 618(C).
    16. Suresh Kumar & Ankit Kumar & Gurcharan Singh, 2023. "Causal relationship among international crude oil, gold, exchange rate, and stock market: Fresh evidence from NARDL testing approach," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 28(1), pages 47-57, January.
    17. Yao, Qiuxiang & Wang, Linyang & Ma, Mingming & Ma, Li & He, Lei & Ma, Duo & Sun, Ming, 2024. "A quantitative investigation on pyrolysis behaviors of metal ion-exchanged coal macerals by interpretable machine learning algorithms," Energy, Elsevier, vol. 300(C).
    18. Hasnain Iftikhar & Josue E. Turpo-Chaparro & Paulo Canas Rodrigues & Javier Linkolk López-Gonzales, 2023. "Forecasting Day-Ahead Electricity Prices for the Italian Electricity Market Using a New Decomposition—Combination Technique," Energies, MDPI, vol. 16(18), pages 1-23, September.
    19. Hilger, Hannes & Witthaut, Dirk & Dahmen, Manuel & Rydin Gorjão, Leonardo & Trebbien, Julius & Cramer, Eike, 2024. "Multivariate scenario generation of day-ahead electricity prices using normalizing flows," Applied Energy, Elsevier, vol. 367(C).
    20. Meng, Anbo & Wang, Peng & Zhai, Guangsong & Zeng, Cong & Chen, Shun & Yang, Xiaoyi & Yin, Hao, 2022. "Electricity price forecasting with high penetration of renewable energy using attention-based LSTM network trained by crisscross optimization," Energy, Elsevier, vol. 254(PA).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:compec:v:63:y:2024:i:6:d:10.1007_s10614-023-10416-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.