IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v16y2023i10p4085-d1146667.html
   My bibliography  Save this article

A Predictive Fuzzy Logic Model for Forecasting Electricity Day-Ahead Market Prices for Scheduling Industrial Applications

Author

Listed:
  • Konstantinos Plakas

    (Electrical and Computer Engineering Department, University of Patras, 26504 Patras, Greece)

  • Ioannis Karampinis

    (Electrical and Computer Engineering Department, University of Patras, 26504 Patras, Greece)

  • Panayiotis Alefragis

    (Electrical and Computer Engineering Department, University of Peloponnese, 26334 Patras, Greece)

  • Alexios Birbas

    (Electrical and Computer Engineering Department, University of Patras, 26504 Patras, Greece)

  • Michael Birbas

    (Electrical and Computer Engineering Department, University of Patras, 26504 Patras, Greece)

  • Alex Papalexopoulos

    (Ecco International Inc., San Francisco, CA 94104, USA)

Abstract

Electricity price forecasting (EPF) has become an essential part of decision-making for energy companies to participate in power markets. As the energy mix becomes more uncertain and stochastic, this process has also become important for industrial companies, as their production schedules are greatly impacted by energy costs. Although various approaches have been tested with varying degrees of success, this study focuses on predicting day-ahead market (DAM) prices in different European markets and how this directly affects the optimal production scheduling for various industrial loads. We propose a fuzzy-based architecture that incorporates the results of two forecasting algorithms; a random forest (RF) and a long short-term memory (LSTM). To enhance the accuracy of the proposed model for a specific country, electricity market data from neighboring countries are also included. The developed DAM price forecaster can then be utilized by energy-intensive industries to optimize their production processes to reduce energy costs and improve energy-efficiency. Specifically, the tool is important for industries with multi-site production facilities in neighboring countries, which could reschedule the production processes depending on the forecasted electricity market price.

Suggested Citation

  • Konstantinos Plakas & Ioannis Karampinis & Panayiotis Alefragis & Alexios Birbas & Michael Birbas & Alex Papalexopoulos, 2023. "A Predictive Fuzzy Logic Model for Forecasting Electricity Day-Ahead Market Prices for Scheduling Industrial Applications," Energies, MDPI, vol. 16(10), pages 1-21, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:10:p:4085-:d:1146667
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/16/10/4085/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/16/10/4085/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Bergmeir, Christoph & Hyndman, Rob J. & Koo, Bonsoo, 2018. "A note on the validity of cross-validation for evaluating autoregressive time series prediction," Computational Statistics & Data Analysis, Elsevier, vol. 120(C), pages 70-83.
    2. Malinauskaite, J. & Jouhara, H. & Ahmad, L. & Milani, M. & Montorsi, L. & Venturelli, M., 2019. "Energy efficiency in industry: EU and national policies in Italy and the UK," Energy, Elsevier, vol. 172(C), pages 255-269.
    3. Maciejowska, Katarzyna & Nitka, Weronika & Weron, Tomasz, 2021. "Enhancing load, wind and solar generation for day-ahead forecasting of electricity prices," Energy Economics, Elsevier, vol. 99(C).
    4. Lago, Jesus & Marcjasz, Grzegorz & De Schutter, Bart & Weron, Rafał, 2021. "Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark," Applied Energy, Elsevier, vol. 293(C).
    5. Abeer Alshejari & Vassilis S. Kodogiannis & Stavros Leonidis, 2020. "Development of Neurofuzzy Architectures for Electricity Price Forecasting," Energies, MDPI, vol. 13(5), pages 1-24, March.
    6. Tarsitano, Agostino & Amerise, Ilaria L., 2017. "Short-term load forecasting using a two-stage sarimax model," Energy, Elsevier, vol. 133(C), pages 108-114.
    7. Javier Pórtoles & Camino González & Javier M. Moguerza, 2018. "Electricity Price Forecasting with Dynamic Trees: A Benchmark Against the Random Forest Approach," Energies, MDPI, vol. 11(6), pages 1-21, June.
    8. Hulshof, Daan & van der Maat, Jan-Pieter & Mulder, Machiel, 2016. "Market fundamentals, competition and natural-gas prices," Energy Policy, Elsevier, vol. 94(C), pages 480-491.
    9. Gürsan, C. & de Gooyert, V., 2021. "The systemic impact of a transition fuel: Does natural gas help or hinder the energy transition?," Renewable and Sustainable Energy Reviews, Elsevier, vol. 138(C).
    10. Sharadga, Hussein & Hajimirza, Shima & Balog, Robert S., 2020. "Time series forecasting of solar power generation for large-scale photovoltaic plants," Renewable Energy, Elsevier, vol. 150(C), pages 797-807.
    11. Jianjun Ouyang & Jie Fu, 2020. "Optimal strategies of improving energy efficiency for an energy-intensive manufacturer considering consumer environmental awareness," International Journal of Production Research, Taylor & Francis Journals, vol. 58(4), pages 1017-1033, February.
    12. Weiwei Cui & Huali Sun & Beixin Xia, 2020. "Integrating production scheduling, maintenance planning and energy controlling for the sustainable manufacturing systems under TOU tariff," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 71(11), pages 1760-1779, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).

    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. Weronika Nitka & Rafał Weron, 2023. "Combining predictive distributions of electricity prices. Does minimizing the CRPS lead to optimal decisions in day-ahead bidding?," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 33(3), pages 105-118.
    2. Ehsani, Behdad & Pineau, Pierre-Olivier & Charlin, Laurent, 2024. "Price forecasting in the Ontario electricity market via TriConvGRU hybrid model: Univariate vs. multivariate frameworks," Applied Energy, Elsevier, vol. 359(C).
    3. Grothe, Oliver & Kächele, Fabian & Krüger, Fabian, 2023. "From point forecasts to multivariate probabilistic forecasts: The Schaake shuffle for day-ahead electricity price forecasting," Energy Economics, Elsevier, vol. 120(C).
    4. Nie, Ying & Li, Ping & Wang, Jianzhou & Zhang, Lifang, 2024. "A novel multivariate electrical price bi-forecasting system based on deep learning, a multi-input multi-output structure and an operator combination mechanism," Applied Energy, Elsevier, vol. 366(C).
    5. Marcjasz, Grzegorz & Narajewski, Michał & Weron, Rafał & Ziel, Florian, 2023. "Distributional neural networks for electricity price forecasting," Energy Economics, Elsevier, vol. 125(C).
    6. Mira Watermeyer & Thomas Mobius & Oliver Grothe & Felix Musgens, 2023. "A hybrid model for day-ahead electricity price forecasting: Combining fundamental and stochastic modelling," Papers 2304.09336, arXiv.org.
    7. Janczura, Joanna & Wójcik, Edyta, 2022. "Dynamic short-term risk management strategies for the choice of electricity market based on probabilistic forecasts of profit and risk measures. The German and the Polish market case study," Energy Economics, Elsevier, vol. 110(C).
    8. 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.
    9. Jiang, Ping & Nie, Ying & Wang, Jianzhou & Huang, Xiaojia, 2023. "Multivariable short-term electricity price forecasting using artificial intelligence and multi-input multi-output scheme," Energy Economics, Elsevier, vol. 117(C).
    10. Oliver Grothe & Fabian Kachele & Fabian Kruger, 2022. "From point forecasts to multivariate probabilistic forecasts: The Schaake shuffle for day-ahead electricity price forecasting," Papers 2204.10154, arXiv.org.
    11. Serafin, Tomasz & Marcjasz, Grzegorz & Weron, Rafał, 2022. "Trading on short-term path forecasts of intraday electricity prices," Energy Economics, Elsevier, vol. 112(C).
    12. Katarzyna Maciejowska & Bartosz Uniejewski & Rafa{l} Weron, 2022. "Forecasting Electricity Prices," Papers 2204.11735, arXiv.org.
    13. Bartosz Uniejewski, 2023. "Smoothing Quantile Regression Averaging: A new approach to probabilistic forecasting of electricity prices," Papers 2302.00411, arXiv.org, revised Nov 2024.
    14. Katarzyna Maciejowska & Weronika Nitka, 2024. "Multiple split approach -- multidimensional probabilistic forecasting of electricity markets," Papers 2407.07795, arXiv.org.
    15. Barja-Martinez, Sara & Aragüés-Peñalba, Mònica & Munné-Collado, Íngrid & Lloret-Gallego, Pau & Bullich-Massagué, Eduard & Villafafila-Robles, Roberto, 2021. "Artificial intelligence techniques for enabling Big Data services in distribution networks: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    16. 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).
    17. Qi Guo & Bruno Remillard & Anatoliy Swishchuk, 2020. "Multivariate General Compound Point Processes in Limit Order Books," Risks, MDPI, vol. 8(3), pages 1-20, September.
    18. Ünal, Berat Berkan & Onaygil, Sermin & Acuner, Ebru & Cin, Rabia, 2022. "Application of energy efficiency obligation scheme for electricity distribution companies in Turkey," Energy Policy, Elsevier, vol. 163(C).
    19. Henriques, Irene & Sadorsky, Perry, 2023. "Forecasting rare earth stock prices with machine learning," Resources Policy, Elsevier, vol. 86(PA).
    20. Billé, Anna Gloria & Gianfreda, Angelica & Del Grosso, Filippo & Ravazzolo, Francesco, 2023. "Forecasting electricity prices with expert, linear, and nonlinear models," International Journal of Forecasting, Elsevier, vol. 39(2), pages 570-586.

    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:gam:jeners:v:16:y:2023:i:10:p:4085-:d:1146667. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.