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Short-Term Load Forecasting of the Greek Power System Using a Dynamic Block-Diagonal Fuzzy Neural Network

Author

Listed:
  • George Kandilogiannakis

    (Department of Informatics and Computer Engineering, Egaleo Park Campus, University of West Attica, 12243 Athens, Greece)

  • Paris Mastorocostas

    (Department of Informatics and Computer Engineering, Egaleo Park Campus, University of West Attica, 12243 Athens, Greece)

  • Athanasios Voulodimos

    (School of Electrical and Computer Engineering, National Technical University of Athens, 15773 Athens, Greece)

  • Constantinos Hilas

    (Department of Computer, Informatics and Telecommunications Engineering, Serres Campus, International Hellenic University, 62124 Serres, Greece)

Abstract

A dynamic fuzzy neural network for short-term load forecasting of the Greek power system is proposed, and an hourly based prediction for the whole year is performed. A DBD-FELF (Dynamic Block-Diagonal Fuzzy Electric Load Forecaster) consists of fuzzy rules with consequent parts that are neural networks with internal recurrence. These networks have a hidden layer, which consists of pairs of neurons with feedback connections between them. The overall fuzzy model partitions the input space in partially overlapping fuzzy regions, where the recurrent neural networks of the respective rules operate. The partition of the input space and determination of the fuzzy rule base is performed via the use of the Fuzzy C-Means clustering algorithm, and the RENNCOM constrained optimization method is applied for consequent parameter tuning. The performance of DBD-FELF is tested via extensive experimental analysis, and the results are promising, since an average percentage error of 1.18% is attained, along with an average yearly absolute error of 76.2 MW. Moreover, DBD-FELF is compared with Deep Learning, fuzzy and neurofuzzy rivals, such that its particular attributes are highlighted.

Suggested Citation

  • George Kandilogiannakis & Paris Mastorocostas & Athanasios Voulodimos & Constantinos Hilas, 2023. "Short-Term Load Forecasting of the Greek Power System Using a Dynamic Block-Diagonal Fuzzy Neural Network," Energies, MDPI, vol. 16(10), pages 1-20, May.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:10:p:4227-:d:1151909
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    References listed on IDEAS

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    Cited by:

    1. Md Fazle Hasan Shiblee & Paraskevas Koukaras, 2025. "Short-Term Load Forecasting in the Greek Power Distribution System: A Comparative Study of Gradient Boosting and Deep Learning Models," Energies, MDPI, vol. 18(19), pages 1-27, September.

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