Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Science and Applied Journal
Aris Triwiyatno
Universitas Diponegoro, Semarang, Central Java, Indonesia
Aerospace Engineering Agriculture, Biological Sciences & Forestry Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Earth & Planetary Sciences Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Neuroscience Nursing Physics Transportation Veterinary Other

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
AI-OPTIMIZED RENEWABLE ENERGY MICROGRIDS: A MACHINE LEARNING APPROACH FOR DYNAMIC LOAD BALANCING IN OFF-GRID COMMUNITIES Enda Wista Sinuraya; Aris Triwiyatno
Science and Applied Journal (SIAP) Vol. 2 No. 1 (2025): Sciences and Applied Journal (SIAP)
Publisher : Penelitian dan Pengembangan Ilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The development of renewable energy-based microgrids is becoming increasingly important in increasing sustainable energy access and reducing dependence on fossil fuels. However, effective energy load management in microgrids remains a major challenge.This research aims to develop and apply machine learning algorithms to optimize dynamic load balancing in renewable energy-based microgrids.This study uses a Systematic Literature Review (SLR) approach to identify and analyze studies related to the application of machine learning in microgrids. Data were collected from academic databases Scopus and Web of Science, and analyzed using thematic coding methods.The results show that machine learning algorithms can be effective in optimizing dynamic load balancing in microgrids. The main findings show that a hybrid approach combining multiple machine learning algorithms can improve prediction accuracy and system stability.This study concludes that the application of machine learning algorithms can be an effective solution in optimizing dynamic load balancing in renewable energy-based microgrids. This finding has important implications in the development of more efficient and sustainable microgrids.
Co-Authors Enda Wista Sinuraya