Enhancing Solar Energy Generation: A Comprehensive Machine Learning-Based PV Prediction and Fault Analysis System for Real-Time Tracking and Forecasting-Reference-Cited by-同舟云学术

Enhancing Solar Energy Generation: A Comprehensive Machine Learning-Based PV Prediction and Fault Analysis System for Real-Time Tracking and Forecasting

Published:2023-12-27 Issue: Volume: Page:1-16
ISSN:1532-5008
Container-title:Electric Power Components and Systems
language:en
Short-container-title:Electric Power Components and Systems

Author:

Karthikeyan G.¹,Jagadeeshwaran A.²

Affiliation:

1. Department of EEE, Knowledge Institute of Technology, Kakapalayam, India

2. Department of EEE, Sona College of Technology, Salem, India

Publisher

Informa UK Limited

Subject

Electrical and Electronic Engineering,Mechanical Engineering,Energy Engineering and Power Technology

Link

https://www.tandfonline.com/doi/pdf/10.1080/15325008.2023.2293947

Reference38 articles.

1. Single-junction organic solar cells with over 19% efficiency enabled by a refined double-fibril network morphology

2. One piece of the puzzle towards 100 Positive Energy Districts (PEDs) across Europe by 2025: An open-source approach to unveil favourable locations of PV-based PEDs from a techno-economic perspective

3. Overview of global status and challenges for end-of-life crystalline silicon photovoltaic panels: A focus on environmental impacts

4. The implementation of grid-connected, residential rooftop photovoltaic systems under different load scenarios in Malaysia

5. CNN-LSTM: An efficient hybrid deep learning architecture for predicting short-term photovoltaic power production

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Efficient detection and analysis of shunt faults in electric power distribution systems (EPDS) using DCFC and EFDC algorithm: a modeling and simulation approach;International Journal of Modelling and Simulation;2024-07-27