Deep Learning Networks for Vectorized Energy Load Forecasting

Kristen Jaskie, Dominique Smith, Andreas Spanias

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

2 Scopus citations

Abstract

Smart energy meters allow individual residential, commercial, and industrial energy load usage to be monitored continuously with high granularity. Accurate short-term energy forecasting is essential for improving energy efficiency, reducing blackouts, and enabling smart grid control and analytics. In this paper, we survey commonly used non-linear deep learning timeseries forecasting methods for this task including long short-term memory recurrent neural networks and nonlinear autoregressive models, nonlinear autoregressive exogenous networks that also include weather data, and for completeness, MATLAB's nonlinear input-output model that only uses weather. These models look at every combination of load sequence data and weather information to identify which factors and methods are most effective at predicting short-term residential load. In this paper, the traditional nonlinear autoregressive model predicted short term load values most accurately using only energy load information with a mean square error of 7.53E-5 and a correlation coefficient of 0.995.

Original languageEnglish (US)
Title of host publication11th International Conference on Information, Intelligence, Systems and Applications, IISA 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9780738123462
DOIs
StatePublished - Jul 15 2020
Event11th International Conference on Information, Intelligence, Systems and Applications, IISA 2020 - Piraeus, Greece
Duration: Jul 15 2020Jul 17 2020

Publication series

Name11th International Conference on Information, Intelligence, Systems and Applications, IISA 2020

Conference

Conference11th International Conference on Information, Intelligence, Systems and Applications, IISA 2020
CountryGreece
CityPiraeus
Period7/15/207/17/20

Keywords

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

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Information Systems and Management
  • Control and Optimization

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