Short-term multivariate time series load data forecasting at low-voltage level using optimised deep-ensemble learning-based models

Ibrahim, IA; Hossain, MJ

Short-term multivariate time series load data forecasting at low-voltage level using optimised deep-ensemble learning-based models

Ibrahim, IA Hossain, MJ

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Energy Conversion and Management, 2023, 296, pp. 1-18
Issue Date:: 2023-11-15

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Field	Value	Language
dc.contributor.author	Ibrahim, IA
dc.contributor.author	Hossain, MJ
dc.date.accessioned	2024-01-05T00:16:00Z
dc.date.available	2024-01-05T00:16:00Z
dc.date.issued	2023-11-15
dc.identifier.citation	Energy Conversion and Management, 2023, 296, pp. 1-18
dc.identifier.issn	0196-8904
dc.identifier.issn	1879-2227
dc.identifier.uri	http://hdl.handle.net/10453/174021
dc.description.abstract	Increasing the renewable energy penetration, especially photovoltaic systems, requires accurate and short-term load forecasting for every individual electricity customer. This can significantly help distributed network service providers (DNSP) plan and operate electrical networks and provide better quality and more reliable electricity services to their customers. Due to the strong volatility of the load data, an optimised deep-ensemble learning methodology is proposed to develop several deep learning models to forecast the load data in individual and aggregate load scenarios. The adaptive wind-driven optimisation (AWDO) algorithm is used to tune the hyperparameters of four of the developed deep learning models, significantly improving their performance. Tuning the deep learning models’ hyperparameters shows significant improvements in the accuracy, as well as training, and testing times. In addition, a hybrid ensemble strategy that contains bagging, random Subspace, and boosting (BRSB) with ensemble pruning is adapted in the optimised deep learning-based models to extract deep features from multivariate data. In this context, the bidirectional long-short-term memory optimised by the AWDO algorithm (Bi-LSTM-AWDO) based hybrid ensemble learning forecasting model is compared exhaustively with several benchmark models, including the recently developed models in the state-of-the-art. The average root mean square error (RMSE), and the average mean absolute percentage error (MAPE) of the Bi-LSTM-AWDO model for individual loads are 0.121 kW, and 7.55%, respectively, while they are 0.025 kW, and 1.51% for aggregate loads, respectively. Accordingly, the Bi-LSTM-AWDO model outperforms other models in forecasting short-term load data for individual and aggregate households using actual smart meters’ measurements.
dc.language	English
dc.publisher	Elsevier
dc.relation.ispartof	Energy Conversion and Management
dc.relation.isbasedon	10.1016/j.enconman.2023.117663
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject	0906 Electrical and Electronic Engineering, 0913 Mechanical Engineering
dc.subject.classification	Energy
dc.subject.classification	4004 Chemical engineering
dc.subject.classification	4008 Electrical engineering
dc.subject.classification	4017 Mechanical engineering
dc.title	Short-term multivariate time series load data forecasting at low-voltage level using optimised deep-ensemble learning-based models
dc.type	Journal Article
utslib.citation.volume	296
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0913 Mechanical Engineering
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/DVC (Research)
pubs.organisational-group	/University of Technology Sydney/DVC (Research)/Institute For Sustainable Futures
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	*
pubs.consider-herdc	false
dc.date.updated	2024-01-05T00:15:58Z
pubs.publication-status	Published
pubs.volume	296

Abstract:

Increasing the renewable energy penetration, especially photovoltaic systems, requires accurate and short-term load forecasting for every individual electricity customer. This can significantly help distributed network service providers (DNSP) plan and operate electrical networks and provide better quality and more reliable electricity services to their customers. Due to the strong volatility of the load data, an optimised deep-ensemble learning methodology is proposed to develop several deep learning models to forecast the load data in individual and aggregate load scenarios. The adaptive wind-driven optimisation (AWDO) algorithm is used to tune the hyperparameters of four of the developed deep learning models, significantly improving their performance. Tuning the deep learning models’ hyperparameters shows significant improvements in the accuracy, as well as training, and testing times. In addition, a hybrid ensemble strategy that contains bagging, random Subspace, and boosting (BRSB) with ensemble pruning is adapted in the optimised deep learning-based models to extract deep features from multivariate data. In this context, the bidirectional long-short-term memory optimised by the AWDO algorithm (Bi-LSTM-AWDO) based hybrid ensemble learning forecasting model is compared exhaustively with several benchmark models, including the recently developed models in the state-of-the-art. The average root mean square error (RMSE), and the average mean absolute percentage error (MAPE) of the Bi-LSTM-AWDO model for individual loads are 0.121 kW, and 7.55%, respectively, while they are 0.025 kW, and 1.51% for aggregate loads, respectively. Accordingly, the Bi-LSTM-AWDO model outperforms other models in forecasting short-term load data for individual and aggregate households using actual smart meters’ measurements.

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