Advanced feature engineering in microgrid PV forecasting: A fast computing and data-driven hybrid modeling framework

Habib, MA; Hossain, MJ

Advanced feature engineering in microgrid PV forecasting: A fast computing and data-driven hybrid modeling framework

Habib, MA Hossain, MJ

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Renewable Energy, 2024, 235, pp. 121258
Issue Date:: 2024-11-01

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Field	Value	Language
dc.contributor.author	Habib, MA
dc.contributor.author	Hossain, MJ
dc.date.accessioned	2024-11-05T01:14:39Z
dc.date.available	2024-11-05T01:14:39Z
dc.date.issued	2024-11-01
dc.identifier.citation	Renewable Energy, 2024, 235, pp. 121258
dc.identifier.issn	0960-1481
dc.identifier.issn	1879-0682
dc.identifier.uri	http://hdl.handle.net/10453/181736
dc.description.abstract	This study introduces an innovative framework designed to forecast the fluctuating short-term generation of photovoltaic (PV) energy in isolated microgrids. The framework relies entirely on solar irradiance data obtained from weather stations located far from the target microgrid. It implements a sophisticated decomposition approach to effectively extract an enriched collection of features from the dataset. Subsequently, a machine learning-based clustering method further enhances the forecasting process by accurately separating the relevant data points. The approach utilizes an advanced two-stage Hybrid Data Linked Model (HDLM) architecture, integrating a Layered Recurrent Neural Framework (LRNF) for prediction and a pattern identification network unit for pattern extraction. This paper demonstrates significant improvements in both the accuracy and effectiveness of estimating PV generation, achieving a mean absolute error of 1.02, a root mean square error of 2.176, and an R-squared score of 0.991. Additionally, the method reduces computing time by 15% after finalizing the input features. A comparative analysis evaluates the superior forecasting capabilities of the HDLM in remote microgrids by benchmarking it against other advanced hybrid deep learning models. The findings highlight the HDLM's potential to greatly enhance and revolutionize the management and operation of renewable energy systems in remote microgrids.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Renewable Energy
dc.relation.isbasedon	10.1016/j.renene.2024.121258
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0906 Electrical and Electronic Engineering, 0913 Mechanical Engineering, 0915 Interdisciplinary Engineering
dc.subject.classification	Energy
dc.subject.classification	40 Engineering
dc.title	Advanced feature engineering in microgrid PV forecasting: A fast computing and data-driven hybrid modeling framework
dc.type	Journal Article
utslib.citation.volume	235
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	0913 Mechanical Engineering
utslib.for	0915 Interdisciplinary Engineering
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	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2024-11-05T01:14:37Z
pubs.publication-status	Accepted
pubs.volume	235

Abstract:

This study introduces an innovative framework designed to forecast the fluctuating short-term generation of photovoltaic (PV) energy in isolated microgrids. The framework relies entirely on solar irradiance data obtained from weather stations located far from the target microgrid. It implements a sophisticated decomposition approach to effectively extract an enriched collection of features from the dataset. Subsequently, a machine learning-based clustering method further enhances the forecasting process by accurately separating the relevant data points. The approach utilizes an advanced two-stage Hybrid Data Linked Model (HDLM) architecture, integrating a Layered Recurrent Neural Framework (LRNF) for prediction and a pattern identification network unit for pattern extraction. This paper demonstrates significant improvements in both the accuracy and effectiveness of estimating PV generation, achieving a mean absolute error of 1.02, a root mean square error of 2.176, and an R-squared score of 0.991. Additionally, the method reduces computing time by 15% after finalizing the input features. A comparative analysis evaluates the superior forecasting capabilities of the HDLM in remote microgrids by benchmarking it against other advanced hybrid deep learning models. The findings highlight the HDLM's potential to greatly enhance and revolutionize the management and operation of renewable energy systems in remote microgrids.

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http://hdl.handle.net/10453/181736