Open AccessImplementing Machine Learning – Artificial Intelligence for Optimizing Solar PV with Conventional Grid
Author(s) -
Arpita De*,
Arunava De
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.f8451.038620
Subject(s) - renewable energy , computer science , sizing , particle swarm optimization , grid , swarm intelligence , photovoltaic system , task (project management) , solar energy , artificial intelligence , industrial engineering , engineering , systems engineering , machine learning , electrical engineering , art , geometry , mathematics , visual arts
The present conventional sources of energy have been rapidly decreasing. There is an ever-increasing demand of energy which can be fulfilled only by taking into consideration, alternative sources of energy that are also environment friendly. For integrating the renewable energy source such as Solar PV with the grid, several factors must be kept in mind for ensuring the health of the grid. In the past, this task was effectively handled with different computational algorithms such as Ant Colony, Particle Swarm Optimization. But with the advent of Big Data technologies and Machine learning techniques, this task is handled even more effectively. This paper will review different studies in which Artificial Intelligence will be used to make effective decisions regarding the load demand, optimal sizing and positioning of Solar PV energy generating stations.