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Development of Neurofuzzy Architectures for Electricity Price Forecasting

Author

Listed:
  • Abeer Alshejari

    (Department of Mathematical Science, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia)

  • Vassilis S. Kodogiannis

    (School of Computer Science & Engineering, University of Westminster, London W1W 6UW, UK)

  • Stavros Leonidis

    (School of Pure and Applied Sciences, Open University of Cyprus, 2220 Nicosia, Cyprus)

Abstract

In 20th century, many countries have liberalized their electricity market. This power markets liberalization has directed generation companies as well as wholesale buyers to undertake a greater intense risk exposure compared to the old centralized framework. In this framework, electricity price prediction has become crucial for any market player in their decision-making process as well as strategic planning. In this study, a prototype asymmetric-based neuro-fuzzy network (AGFINN) architecture has been implemented for short-term electricity prices forecasting for ISO New England market. AGFINN framework has been designed through two different defuzzification schemes. Fuzzy clustering has been explored as an initial step for defining the fuzzy rules while an asymmetric Gaussian membership function has been utilized in the fuzzification part of the model. Results related to the minimum and maximum electricity prices for ISO New England, emphasize the superiority of the proposed model over well-established learning-based models.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:5:p:1209-:d:329073
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    References listed on IDEAS

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    2. Karolina Kudelina & Bilal Asad & Toomas Vaimann & Anton Rassõlkin & Ants Kallaste & Huynh Van Khang, 2021. "Methods of Condition Monitoring and Fault Detection for Electrical Machines," Energies, MDPI, vol. 14(22), pages 1-20, November.
    3. 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.
    4. 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).
    5. Štefan Bojnec & Alan Križaj, 2021. "Electricity Markets during the Liberalization: The Case of a European Union Country," Energies, MDPI, vol. 14(14), pages 1-21, July.

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