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- W4234007273 abstract "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." @default.
- W4234007273 created "2022-05-12" @default.
- W4234007273 date "2020-03-30" @default.
- W4234007273 modified "2023-10-16" @default.
- W4234007273 title "Implementing Machine Learning – Artificial Intelligence for Optimizing Solar PV with Conventional Grid" @default.
- W4234007273 doi "https://doi.org/10.35940/ijrte.f8451.038620" @default.
- W4234007273 hasPublicationYear "2020" @default.
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