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

George Kandilogiannakis, Paris Mastorocostas (), Athanasios Voulodimos and Constantinos Hilas
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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

Energies, 2023, vol. 16, issue 10, 1-20

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.

Keywords: Greek power system; electric load forecasting; block-diagonal neurons; fuzzy neural network; internal feedback (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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