Day ahead power demand forecasting for hybrid power at the edge

Calum McCormack, Christopher Wallace, Peter Barrie, Gordon Morison

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

1 Citation (Scopus)
112 Downloads (Pure)

Abstract

We describe the investigation and testing of univariate forecasting techniques on IoT hardware for application at “the Edge” using power demand forecasting. An evaluation of common forecasting techniques is presented, tested using the Morocco Buildings Electricity Consumption Datasets. An architecture is described for the Edge system that would enable 1-dayforward forecasts of power demand for use in provisioning power in a hybrid power system. Several of the configurations examined in this study performed comparably with current trends in forecasting methods and are suitable for this application at the Edge, providing a balance of performance and accuracy. A Long Short-Term Memory (LSTM) Neural Network configuration provided the most effective balance of performance, accuracy and simplicity of deployment that is desirable for an application at the Edge.

Original languageEnglish
Title of host publication2022 IEEE World AI IoT Congress (AIIoT)
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages437-441
Number of pages5
ISBN (Electronic)9781665484534
ISBN (Print)9781665484541
DOIs
Publication statusPublished - 13 Jul 2022
EventWorld AI IoT Congress 2022 - Seattle, United States
Duration: 6 Jun 20229 Jun 2022

Publication series

Name
ISSN (Print)None

Conference

ConferenceWorld AI IoT Congress 2022
Abbreviated titleIEEE AIIoT 2022
Country/TerritoryUnited States
CitySeattle
Period6/06/229/06/22

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Safety, Risk, Reliability and Quality

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