Predicting customer behaviors on energy consumption: Why past usage data are not enough?

Supadchaya Puangpontip, Rattikorn Hewett

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Smart Grid is an important cyber-physical infrastructure for power generation, transmission and distribution. As energy becomes a necessity for our modern living, effective management of Smart Grid can have great impact on power services. Recent smart meter technology has provided an opportunity that allows an economic model of utility to be more flexible and optimal. Construction of such a model requires better understanding of consumer behaviors in response to a given pricing. Most existing research uses mathematical models that tend to rely on assumptions that may not be realistically realizable. Majority of empirical work aims to predict or optimize utility loads. To the best of our knowledge, no empirical published work has addressed this issue. Our research aims to find an appropriate Big data analytic approach to learning consumption pattern of each household at appliance level. Particularly, this paper investigates how machine learning can be used to predict the change of customer's consumption habits for lower pricing. The problem is non-trivial due to conflicting dependent factors that are dynamically changing. Our preliminary results, obtained from three popular machine-learning models using synthesized data, show relatively high accuracy ranging from 61.5% to 99.4% for various appliances. Although the results are encouraging, we provide insights towards the validity and improvement of these predictive models.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018
EditorsYang Song, Bing Liu, Kisung Lee, Naoki Abe, Calton Pu, Mu Qiao, Nesreen Ahmed, Donald Kossmann, Jeffrey Saltz, Jiliang Tang, Jingrui He, Huan Liu, Xiaohua Hu
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages4577-4581
Number of pages5
ISBN (Electronic)9781538650356
DOIs
StatePublished - Jan 22 2019
Event2018 IEEE International Conference on Big Data, Big Data 2018 - Seattle, United States
Duration: Dec 10 2018Dec 13 2018

Publication series

NameProceedings - 2018 IEEE International Conference on Big Data, Big Data 2018

Conference

Conference2018 IEEE International Conference on Big Data, Big Data 2018
Country/TerritoryUnited States
CitySeattle
Period12/10/1812/13/18

Keywords

  • big data analytics
  • cyber-physical system
  • demand response
  • machine learning
  • smart grid

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