Electricity Price Forecasting using Convolution and LSTM Models

Mittal, DA; Liu, S; Xu, G

Electricity Price Forecasting using Convolution and LSTM Models

Mittal, DA Liu, S Xu, G

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of 2020 7th IEEE International Conference on Behavioural and Social Computing, BESC 2020, 2020, 00, pp. 1-4
Issue Date:: 2020-11-05

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Field	Value	Language
dc.contributor.author	Mittal, DA
dc.contributor.author	Liu, S
dc.contributor.author	Xu, G https://orcid.org/0000-0003-4493-6663
dc.date	2020-11-05
dc.date.accessioned	2022-01-19T00:24:44Z
dc.date.available	2022-01-19T00:24:44Z
dc.date.issued	2020-11-05
dc.identifier.citation	Proceedings of 2020 7th IEEE International Conference on Behavioural and Social Computing, BESC 2020, 2020, 00, pp. 1-4
dc.identifier.isbn	9781728186054
dc.identifier.uri	http://hdl.handle.net/10453/153321
dc.description.abstract	Electricity Market uses Demand and Supply chain strategy. Also, it is prone to random fluctuations that directly impact profit. Therefore forecasting demand becomes very important to mitigate the consequences of price dynamics. This paper proposes a Deep Learning model using Long Short Term Memory (LSTM) and Convolution Neural Network to forecast future electricity prices on the Australian electricity market and compares them with other state of the art models. We have selected evaluation metrics to prove that our model outperforms the other existing models for electricity price prediction.
dc.language	en
dc.publisher	IEEE
dc.relation	http://purl.org/au-research/grants/arc/LP170100891
dc.relation	Providence Future Pty Ltd
dc.relation.ispartof	Proceedings of 2020 7th IEEE International Conference on Behavioural and Social Computing, BESC 2020
dc.relation.ispartof	2020 7th International Conference on Behavioural and Social Computing (BESC)
dc.relation.isbasedon	10.1109/BESC51023.2020.9348313
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Electricity Price Forecasting using Convolution and LSTM Models
dc.type	Conference Proceeding
utslib.citation.volume	00
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2023-02-28T00:00:00+1000Z
dc.date.updated	2022-01-19T00:24:43Z
pubs.finish-date	2020-11-07
pubs.publication-status	Published
pubs.start-date	2020-11-05
pubs.volume	00

Abstract:

Electricity Market uses Demand and Supply chain strategy. Also, it is prone to random fluctuations that directly impact profit. Therefore forecasting demand becomes very important to mitigate the consequences of price dynamics. This paper proposes a Deep Learning model using Long Short Term Memory (LSTM) and Convolution Neural Network to forecast future electricity prices on the Australian electricity market and compares them with other state of the art models. We have selected evaluation metrics to prove that our model outperforms the other existing models for electricity price prediction.

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http://hdl.handle.net/10453/153321