Logo Lanfrica
  • Accueil
  • Atlas
  • Analyses
  • Documentation
  • Sign in

© 2026 Lanfrica. Tous droits réservés. Tous les droits d'auteur des ressources affichées sur le site Web Lanfrica appartiennent aux détenteurs de droits d'auteur d'origine, sauf indication contraire explicite.

Comparative Application of Artificial Neural Networks and ANFIS Techniques for Short-Term Load Forecasting in the Western Libyan Power Grid

Domaine:

environment and energy

Type de record:

paper
Créateur:
EltIhbFor
Éditeur:
Zenodo
Hôte:avatar

The stability and economic efficiency of modern power systems rely profoundly on accurate short-term load forecasting (STLF). This investigation presents a comparative assessment of two artificial intelligence methodologies,Artificial Neural Networks (ANN) and the Adaptive Neuro-Fuzzy Inference System (ANFIS) for STLF within the Western Libyan power grid. This network operates under considerable strain from extreme climatic conditions and infrastructural limitations, which introduce pronounced volatility and non-linearity into load patterns. Leveraging a comprehensive 2023 dataset from the General Electricity Company of Libya (GECOL), which integrates historical load data with critical meteorological variables, two models in MATLAB were developed and simulated. The findings reveal a decisive superiority of the ANFIS model, which achieved a remarkable average forecasting error of just 0.50%, starkly contrasting with the ANN model's error of 8.37%. This performance is attributed to the ANFIS architecture, which effectively marries the adaptive learning capabilities of neural networks with the transparent, rule-based reasoning of fuzzy logic. This synergy renders ANFIS an exceptionally accurate tool for short-term load forecasting in complex and uncertain environments like Libya.

Visit

doi.org

Tags

Short-Term Load Forecasting (STLF), Artificial Neural Networks (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), weather data, Libya, MATLAB

Licenses

info:eu-repo/semantics/openAccessCreative Commons Attribution 4.0 Internationalhttps://creativecommons.org/licenses/by/4.0/legalcode

Similaires

Application of Artificial Neural Networks and Fuzzy Logic Methods for Short-Term Load Forecasting of the Western Libyan Electric NetworkUse of Artificial Neural Networks for Short-Term Electricity Load Forecasting of Kenya National Grid Power SystemForecasting Short-Term Peak Load Demand in the Libyan Power Grid using Multiple Regression ModelA Fuzzy Logic Model for Short-Term Load Forecasting in the Libyan Power NetworkInterval Type-2 Fuzzy Neural Networks for Short Term Electric Load Forecasting: A Comparative StudyEffective load forecasting for large power consuming industrial customers using long short-term memory recurrent neural networks

Application of Artificial Neural Networks and Fuzzy Logic Methods for Short-Term Load Forecasting of the Western Libyan Electric Network

This research paper investigates the application of artificial neural networks (ANNs) and fuzzy logi

Use of Artificial Neural Networks for Short-Term Electricity Load Forecasting of Kenya National Grid Power System

Forecasting Short-Term Peak Load Demand in the Libyan Power Grid using Multiple Regression Model

The demand for energy, particularly electricity, has been rising rapidly around the world and plays

A Fuzzy Logic Model for Short-Term Load Forecasting in the Libyan Power Network

Short-term load forecasting is an essential system for predicting electricity demand, with a lead ti

Interval Type-2 Fuzzy Neural Networks for Short Term Electric Load Forecasting: A Comparative Study

This paper focuses on the study of short term load forecasting (STELF) using interval Type-2 Fuzzy L

Effective load forecasting for large power consuming industrial customers using long short-term memory recurrent neural networks

The study of South African distribution (Dx.) network’s load forecasting using recent and state of t