Deep Learning Based Short- Term Load Forecasting for Urban Areas

M. Maksut, A. Karbozov, M. Myrzaliyeva, H. S.V.S.Kumar Nunna, Prashant K. Jamwal, Suryanarayana Doolla

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper proposes a short-term load forecasting for residential applications. Deep learning is considered to be a powerful method in forecasting electricity load. As the present state of deep learning is still in progress, new updates towards improving the accuracy of the load forecasting are significantly important. Therefore, this paper proposes a restricted Boltzmann pre-training method and a rectifier linear unit method to enhance the current structure of Deep Neural Network (DNN) method. Moreover, making a priority list of factors that influence residential electricity consumption based on a location (London) is performed and analyzed in the paper.

Original languageEnglish
Title of host publication2019 IEEE Industry Applications Society Annual Meeting, IAS 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538645390
DOIs
Publication statusPublished - Sep 2019
Event2019 IEEE Industry Applications Society Annual Meeting, IAS 2019 - Baltimore, United States
Duration: Sep 29 2019Oct 3 2019

Publication series

Name2019 IEEE Industry Applications Society Annual Meeting, IAS 2019

Conference

Conference2019 IEEE Industry Applications Society Annual Meeting, IAS 2019
CountryUnited States
CityBaltimore
Period9/29/1910/3/19

Keywords

  • Artificial Neural Network (ANN)
  • Deep Neural Network (DNN)
  • Rectifier Linear Unit (ReLU)
  • restricted Boltzmann machine pre-training
  • Short term load forecasting (STLF)

ASJC Scopus subject areas

  • Filtration and Separation
  • Fluid Flow and Transfer Processes
  • Process Chemistry and Technology
  • Energy Engineering and Power Technology
  • Electrical and Electronic Engineering
  • Industrial and Manufacturing Engineering
  • Transportation

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