Enhanced Electricity Demand Forecasting in Australia Using a CNN-LSTM Model with Heating and Cooling Degree Days Data

AlSmadi, L; Lei, G; Li, L

Enhanced Electricity Demand Forecasting in Australia Using a CNN-LSTM Model with Heating and Cooling Degree Days Data

AlSmadi, L Lei, G

Li, L

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2023 IEEE International Future Energy Electronics Conference (IFEEC), 2024, 00, pp. 504-508
Issue Date:: 2024-03-19

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Field	Value	Language
dc.contributor.author	AlSmadi, L
dc.contributor.author	Lei, G https://orcid.org/0000-0002-8369-6802
dc.contributor.author	Li, L https://orcid.org/0000-0001-8485-2216
dc.date	2023-11-20
dc.date.accessioned	2024-05-14T04:14:44Z
dc.date.available	2024-05-14T04:14:44Z
dc.date.issued	2024-03-19
dc.identifier.citation	2023 IEEE International Future Energy Electronics Conference (IFEEC), 2024, 00, pp. 504-508
dc.identifier.isbn	979-8-3503-3989-5
dc.identifier.uri	http://hdl.handle.net/10453/178961
dc.description.abstract	In this paper we present a hybrid deeplearning forecasting method based on heating and cooling degree days data to predict the electricity demand in Australia The proposed model integrates convolutional neural network CNN and long shortterm memory LSTM models to improve electricity prediction accuracy The proposed model performance is comprehensively compared with a few conventional forecast methods including deep neural networks DNN model The results show that the mean absolute error and mean absolute percentage error of the prediction have been reduced by using the proposed hybrid model
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2023 IEEE International Future Energy Electronics Conference (IFEEC)
dc.relation.ispartof	2023 IEEE International Future Energy Electronics Conference
dc.relation.isbasedon	10.1109/ifeec58486.2023.10458533
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Enhanced Electricity Demand Forecasting in Australia Using a CNN-LSTM Model with Heating and Cooling Degree Days Data
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Sydney, Australia
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/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	closed_access	*
dc.date.updated	2024-05-14T04:14:43Z
pubs.finish-date	2023-11-23
pubs.place-of-publication	Piscataway, USA
pubs.publication-status	Published
pubs.start-date	2023-11-20
pubs.volume	00
dc.location	Piscataway, USA

Abstract:

In this paper we present a hybrid deeplearning forecasting method based on heating and cooling degree days data to predict the electricity demand in Australia The proposed model integrates convolutional neural network CNN and long shortterm memory LSTM models to improve electricity prediction accuracy The proposed model performance is comprehensively compared with a few conventional forecast methods including deep neural networks DNN model The results show that the mean absolute error and mean absolute percentage error of the prediction have been reduced by using the proposed hybrid model

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http://hdl.handle.net/10453/178961