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Data-Driven Methods of Machine Learning in Modeling the Smart Grids

EasyChair Preprint no. 8273

7 pagesDate: June 15, 2022


Electricity demand is rising in lockstep with global population growth. The present power system, which is almost a century old, faces numerous issues in maintaining a steady supply of electricity from huge power plants to customers. To meet these issues, the electricity industry has enthusiastically embraced the new smart grid concept proposed by engineers. If we can provide a secure smart grid, this movement will be more useful and sustainable. Machine learning, which is a relatively recent era of information technology, has the potential to make smart grids extremely safe. This paper is a literature survey of the application of machine learning in different areas of smart grids. This paper concludes by mentioning the best machine learning algorithms that can be used in different aspects of the smart grid.

Keyphrases: machine learning, Machine Learning Algorithms, Smart Grid

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Rituraj Rituraj and Diana Ecker and Annamaria Varkonyi Koczy},
  title = {Data-Driven Methods of Machine Learning in Modeling the Smart Grids},
  howpublished = {EasyChair Preprint no. 8273},

  year = {EasyChair, 2022}}
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