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

نویسندگان

چکیده

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

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ژورنال

عنوان ژورنال: Energies

سال: 2023

ISSN: ['1996-1073']

DOI: https://doi.org/10.3390/en16104227