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

被引：2
|
作者：
Kandilogiannakis, George [1 ]
Mastorocostas, Paris [1 ]
Voulodimos, Athanasios [2 ]
Hilas, Constantinos [3 ]
机构：
[1] Univ West Attica, Dept Informat & Comp Engn, Egaleo Pk Campus, Athens 12243, Greece
[2] Natl Tech Univ Athens, Sch Elect & Comp Engn, Athens 15773, Greece
[3] Int Hellen Univ, Dept Comp Informat & Telecommun Engn, Serres Campus, Serres 62124, Greece
来源：
ENERGIES | 2023年 / 16卷 / 10期
关键词：
Greek power system; electric load forecasting; block-diagonal neurons; fuzzy neural network; internal feedback; RECURRENT; ALGORITHM; ANFIS; MODEL;
D O I：
10.3390/en16104227
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
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.
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页数：20
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