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Residential Energy Management: a Machine Learning Perspective

EasyChair Preprint no. 4895

6 pagesDate: January 12, 2021


In smart grids, residential energy management is a vital part of demand-side management. It plays a pivotal role in improving the efficiency and sustainability of the power system. However, challenges such as variability of consumption profiles require machine learning to understand and forecast residential demands. Moreover, machine learning based intelligent load management is required for effective implementation of demand response programs. In this article, applications of machine learning algorithms in residential demand forecasting, load profiling, consumer characterization, and load management are comprehensively discussed. The article also examines the characteristics and availability of relevant databases, and explores research challenges and possibilities.

Keyphrases: demand response, load forecasting, machine learning, Residential energy management, Smart Grids

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Mahmood Reaz Sunny and Md Ahsan Kabir and Intisar Tahmid Naheen and Md Tanvir Ahad},
  title = {Residential Energy Management: a Machine Learning Perspective},
  howpublished = {EasyChair Preprint no. 4895},

  year = {EasyChair, 2021}}
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