Optimization of Solar Panel Deployment Using Machine Learning. (16th May 2022)

• Record Type: Journal Article
• Authors: Kamal, Shoaib; Ramapraba, P. S.; Kumar, Avinash; Saha, Bikash Chandra; Lakshminarayana, M.; Sanal Kumar, S.; Gopalan, Anitha; Erko, Kuma Gowwomsa
• Other Names: Mohanavel V. Academic Editor.
• Is Part Of: International journal of photoenergy. Volume 2022(2022)
• Publication Date: 2022-05-16
• Subjects: Photochemistry -- Periodicals; Photobiology -- Periodicals; Chemistry, Physical and theoretical -- Periodicals; Photochimie; Photobiologie; Chimie physique et théorique; Chemistry, Physical and theoretical; Photobiology; Photochemistry; Electronic journals; Periodicals; 541.35
• DOI: 10.1155/2022/7249109
• Languages: English
• ISSNs: 1110-662X

Abstract

Abstract : In this work, we proposed a mechanism for topology reconfiguration or optimization of photovoltaic (PV) arrays using machine learning-assisted techniques. The study takes into concern several topologies that includes series parallel topology, parallel topology, bridge link topology, honeycomb topology, and total cross tied. The artificial neural network-based topology reconfiguration strategy allows for optimal working conditions for PV arrays. With this, machine learning-assisted topology reconfiguration or optimal solar panel deployment enables the proposed mechanism to achieve higher degree of testing accuracy precision, recall, and f-measure under standard ideal condition.