Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering
Pannesai, Ismail Yusuf
Unknown Affiliation
Computer Science & IT Electrical & Electronics Engineering

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

Found 1 Documents
Search

 1 
Systematic review of artificial intelligence applications in predicting solar photovoltaic power production efficiency Ikhsan, M. Rizki; Lakulu, Muhammad Modi; Pannesai, Ismail Yusuf; Rizali, Muhammad; Nugraha, Bayu; Swastina, Liliana
International Journal of Electrical and Computer Engineering (IJECE) Vol 16, No 1: February 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v16i1.pp463-476

Abstract

The global energy crisis and climate change demand more accurate and efficient renewable energy forecasting methods. Solar photovoltaic (PV) systems offer abundant clean energy but their efficiency is highly affected by weather variability, requiring advanced predictive models. This systematic review of 69 studies published between 2020 and 2024 evaluates artificial intelligence (AI) and machine learning (ML) applications in PV forecasting, with a focus on hybrid algorithms such as convolutional neural network-long short-term memory (CNN-LSTM). Results demonstrate that hybrid models consistently outperform traditional statistical methods and standalone AI approaches by capturing spatiotemporal patterns more effectively, achieving significant error reductions and improving reliability. A notable gap identified is the limited integration of consumer behavior into forecasting models, despite evidence that incorporating demand-side patterns enhances accuracy. Challenges also remain in data availability, scalability across diverse climates, and computational requirements. This review contributes by synthesizing recent advances and emphasizing consumer integration as an underexplored but critical dimension for future research. The findings provide a foundation for developing more precise, resilient, and scalable PV forecasting models, supporting optimized energy management and accelerating the transition toward sustainable energy systems.
Co-Authors Bayu Nugraha Ikhsan, M. Rizki Lakulu, Muhammad Modi Liliana Swastina Rizali, Muhammad