Predicting Demand in IoT Enabled Service Stations

Himadri Sikhar Khargharia, Siddhartha Shakya, Russell Ainslie, Sara AlShizawi, Gilbert Owusu

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

5 Scopus citations

Abstract

The current world of AI revolves around forecasting and prediction, eating up a major chunk of problem statements generated by organizations in various domains. Applying forecasting techniques to get an advance view of the number of visitors per bay in a service station will help the owners to plan and adjust their resources for increased operational efficiency as well as to manage any sudden change in visitor demand. As part of this paper, we create a prediction model for forecasting expected visitors demand for a service station. We compare the results of applying various machine learning and statistical methods using historical visitor data as well as historical weather data. The result states that, among the techniques used, neural network using both historical and weather data performs best, providing a future view of expected demand with high accuracy.

Original languageBritish English
Title of host publicationProceedings - 2019 IEEE Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2019
EditorsGalina L. Rogova, Nicolette McGeorge, Odd Erik Gundersen, Kellyn Rein, Mary Freiman
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages81-87
Number of pages7
ISBN (Electronic)9781538695999
DOIs
StatePublished - Apr 2019
Event2019 IEEE Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2019 - Las Vegas, United States
Duration: 8 Apr 201911 Apr 2019

Publication series

NameProceedings - 2019 IEEE Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2019

Conference

Conference2019 IEEE Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2019
Country/TerritoryUnited States
CityLas Vegas
Period8/04/1911/04/19

Keywords

  • arima
  • elastic net
  • kneighbors regressor
  • moving average
  • neural network
  • svr

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