Machine Learning for Fast Short-Term Energy Load Forecasting

Dominique Smith, Kristen Jaskie, John Cadigan, Joseph Marvin, Andreas Spanias

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

1 Scopus citations

Abstract

Improved energy usage data from smart meters offers a unique opportunity to apply advanced analytics that can dramatically improve load forecasting. Utility companies, policy makers, and consumers benefit with better integration of renewables and overall energy management in the IoT digital age. Accurate short-term energy forecasting is essential to improving energy efficiency, reducing blackouts, and enabling smart grid control. In this work-in-progress (WIP) paper, we use individual residential load data to perform customer segmentation based on energy profiles, introduce a unique data segmentation and feature extraction technique based on inherent load signal periodicities, and use deep learning to perform fast and accurate short-term forecasting. Partnering with Prime Solutions Group, a veteran-owned company based in Arizona, we found that we could obtain up to a 12% improvement in hourly one-day forecasting using our custom data segmentation and feature extraction techniques with neural network methods.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE Conference on Industrial Cyberphysical Systems, ICPS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages433-436
Number of pages4
ISBN (Electronic)9781728163895
DOIs
StatePublished - Jun 10 2020
Event3rd IEEE Conference on Industrial Cyberphysical Systems, ICPS 2020 - Virtual, Tampere, Finland
Duration: Jun 10 2020Jun 12 2020

Publication series

NameProceedings - 2020 IEEE Conference on Industrial Cyberphysical Systems, ICPS 2020

Conference

Conference3rd IEEE Conference on Industrial Cyberphysical Systems, ICPS 2020
CountryFinland
CityVirtual, Tampere
Period6/10/206/12/20

Keywords

  • Load Forecasting
  • LSTM
  • Machine Learning
  • Neural Networks
  • Smart Grid

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Hardware and Architecture
  • Information Systems and Management
  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering
  • Safety, Risk, Reliability and Quality

Fingerprint Dive into the research topics of 'Machine Learning for Fast Short-Term Energy Load Forecasting'. Together they form a unique fingerprint.

Cite this