ReNFuzz-LF: A Recurrent Neurofuzzy System for Short-Term Load Forecasting
A neurofuzzy system is proposed for short-term electric load forecasting. The fuzzy rule base of ReNFuzz-LF consists of rules with dynamic consequent parts that are small-scale recurrent neural networks with one hidden layer, whose neurons have local output feedback. The particular representation ma...
Main Authors: | George Kandilogiannakis, Paris Mastorocostas, Athanasios Voulodimos |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2022-05-01
|
Series: | Energies |
Subjects: | |
Online Access: | https://www.mdpi.com/1996-1073/15/10/3637 |
Similar Items
-
Development of Neurofuzzy Architectures for Electricity Price Forecasting
by: Abeer Alshejari, et al.
Published: (2020-03-01) -
Short-Term Load Forecasting of the Greek Power System Using a Dynamic Block-Diagonal Fuzzy Neural Network
by: George Kandilogiannakis, et al.
Published: (2023-05-01) -
Feedback-Linearization-Based Fuel-Cell Adaptive-Control Paradigm in a Microgrid Using a Wavelet-Entrenched NeuroFuzzy Framework
by: Muhammad Awais, et al.
Published: (2021-03-01) -
Adaptive Feedback Linearization Based NeuroFuzzy Maximum Power Point Tracking for a Photovoltaic System
by: Sidra Mumtaz, et al.
Published: (2018-03-01) -
Hybridising Neurofuzzy Model the Seasonal Autoregressive Models for Electricity Price Forecasting on Germany’s Spot Market
by: Dorel Mihai Paraschiv, et al.
Published: (2023-05-01)