Evaluating and comparing machine learning approaches for effective decision making in renewable microgrid systems-Reference-Cited by-同舟云学术

Evaluating and comparing machine learning approaches for effective decision making in renewable microgrid systems

Published:2024-03 Issue: Volume:21 Page:101888
ISSN:2590-1230
Container-title:Results in Engineering
language:en
Short-container-title:Results in Engineering

Author:

Elabbassi Ismail^ORCID,Khala Mohamed,El yanboiy Naima,Eloutassi Omar,El hassouani Youssef

Publisher

Elsevier BV

Reference68 articles.

1. Hierarchical model predictive control for islanded and grid-connected microgrids with wind generation and hydrogen energy storage systems;Abdelghany;Int. J. Hydrogen Energy,2024

2. Economic and environmental factors based multi-objective approach for optimizing energy management in a microgrid;Chakraborty;Renew. Energy,2024

3. Adaptive neural Fuzzy inference system (ANFIS) in a grid connected-fuel cell-electrolyser-solar PV-Battery-Super capacitor energy storage system management;Elabbassi,2023

4. Hybrid energy system integration and management for solar energy: a review;Falope;Energy Convers. Manag. X,2024

5. Wind-solar-storage trade-offs in a decarbonizing electricity system;Gangopadhyay;Appl. Energy,2024

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