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Short Term Load Forecasting Using TabNet: A Comparative Study with Traditional State-of-the-Art Regression Models
Engineering Proceedings, Volume: 5, Issue: 1, Start page: 6
Swansea University Authors:
Eugenio Borghini, Cinzia Giannetti
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DOI (Published version): 10.3390/engproc2021005006
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...
| Published in: | Engineering Proceedings |
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| ISSN: | 2673-4591 |
| Published: |
MDPI AG
2021
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa57695 |
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| 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. |
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| Keywords: |
short-term electricity demand forecasting; neural networks; TabNet |
| College: |
Faculty of Science and 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 |