No Cover Image

Journal article 25 views 6 downloads

Short Term Load Forecasting Using TabNet: A Comparative Study with Traditional State-of-the-Art Regression Models / Eugenio Borghini, Cinzia Giannetti

Engineering Proceedings, Volume: 5, Issue: 1, Start page: 6

Swansea University Authors: Eugenio Borghini, Cinzia Giannetti

  • 57695.pdf

    PDF | Version of Record

    © 2021 by the authors.This is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license

    Download (463.87KB)

Abstract

Electric load forecasting is becoming increasingly challenging due to the growing penetration of decentralised energy generation and power-electronics based loads such as heat pumps and electric vehicles, which adds to a transition to more variable work patterns (accentuated by the COVID-19 pandemic...

Full description

Published in: Engineering Proceedings
ISSN: 2673-4591
Published: MDPI AG 2021
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa57695
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract: Electric load forecasting is becoming increasingly challenging due to the growing penetration of decentralised energy generation and power-electronics based loads such as heat pumps and electric vehicles, which adds to a transition to more variable work patterns (accentuated by the COVID-19 pandemic in 2020). In this paper, three different Machine Leaning models are analysed to predict the energy load one week ahead for a period of time including the COVID-19 pandemic. It is shown that, by using the recently proposed TabNet model architecture, it is possible to achieve an accuracy comparable to more traditional approaches based on gradient boosting and artificial neural networks without the need of performing complex feature engineering.
Keywords: short-term electricity demand forecasting; neural networks; TabNet
College: College of Engineering
Funders: This research was funded by the UK Engineering and Physical Sciences Research Council (EPSRC) project EP/S001387/1 and the European Regional Development Funds projects IMPACT
Issue: 1
Start Page: 6