Machine Learning Based Solar Photovoltaic Power Forecasting: A Review and Comparison-Reference-Cited by-同舟云学术

Machine Learning Based Solar Photovoltaic Power Forecasting: A Review and Comparison

Published:2023 Issue: Volume:11 Page:40820-40845
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Gaboitaolelwe Jwaone¹,Zungeru Adamu Murtala¹^ORCID,Yahya Abid¹^ORCID,Lebekwe Caspar K.¹^ORCID,Vinod Dasari Naga²^ORCID,Salau Ayodeji Olalekan³^ORCID

Affiliation:

1. Department of Electrical, Computer and Telecommunications Engineering, Botswana International University of Science and Technology, Palapye, Botswana

2. Department of Electronics and Communication Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Avadi, Chennai, Tamil Nadu, India

3. Department of Electrical/Electronic and Computer Engineering, Afe Babalola University, Ado Ekiti, Nigeria

Funder

Office of Research, Development, and Innovation (ORDI) of the Botswana International University of Science and Technology

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10107594.pdf?arnumber=10107594

Reference87 articles.

1. Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power

2. The optimized artificial neural network model with Levenberg–Marquardt algorithm for global solar radiation estimation in eastern Mediterranean region of Turkey;teke;J Cleaner Prod,2016