Joint Matrix Decomposition-Based Missing Data Completion in Low-Voltage Area

In-depth mining and analysis of electricity data in low-voltage area are essential for the further intelligent development of power grids. However, in the actual data collection and measurement of low-voltage area, there will be missing data, and complete electricity data cannot be obtained. To obta...

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Autores principales:	Haowen Wu, Chen Yang, Wenwang Xie, Wei Zhang
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
Acceso en línea:	https://doaj.org/article/bca3a193048d4b3a818b3590cc84aee4
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spelling	oai:doaj.org-article:bca3a193048d4b3a818b3590cc84aee42021-11-22T01:11:24ZJoint Matrix Decomposition-Based Missing Data Completion in Low-Voltage Area1563-514710.1155/2021/4170064https://doaj.org/article/bca3a193048d4b3a818b3590cc84aee42021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/4170064https://doaj.org/toc/1563-5147In-depth mining and analysis of electricity data in low-voltage area are essential for the further intelligent development of power grids. However, in the actual data collection and measurement of low-voltage area, there will be missing data, and complete electricity data cannot be obtained. To obtain complete power data, this paper proposes a low-voltage station area missing data complement model based on joint matrix decomposition. First, we analyse the characteristics of the low-pressure station data. Then, a model that comprehensively considers the characteristics of the low-voltage station area data is proposed, which includes three parts: the construction of a low-voltage station area data tensor, the joint matrix decomposition, and the completion of the missing data, and it is named LPZ. After that, the CIM learning algorithm proposed in this paper is used to iteratively solve the model to obtain the completed data. Finally, the method proposed in this paper is used to complement the two situations of random loss and all-day loss of real current data in a low-voltage station area and compared with the traditional complement method. The experimental results show that this method is not only effective but also that the completion effect is better than that of other completion methods.Haowen WuChen YangWenwang XieWei ZhangHindawi LimitedarticleEngineering (General). Civil engineering (General)TA1-2040MathematicsQA1-939ENMathematical Problems in Engineering, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939
spellingShingle	Engineering (General). Civil engineering (General) TA1-2040 Mathematics QA1-939 Haowen Wu Chen Yang Wenwang Xie Wei Zhang Joint Matrix Decomposition-Based Missing Data Completion in Low-Voltage Area
description	In-depth mining and analysis of electricity data in low-voltage area are essential for the further intelligent development of power grids. However, in the actual data collection and measurement of low-voltage area, there will be missing data, and complete electricity data cannot be obtained. To obtain complete power data, this paper proposes a low-voltage station area missing data complement model based on joint matrix decomposition. First, we analyse the characteristics of the low-pressure station data. Then, a model that comprehensively considers the characteristics of the low-voltage station area data is proposed, which includes three parts: the construction of a low-voltage station area data tensor, the joint matrix decomposition, and the completion of the missing data, and it is named LPZ. After that, the CIM learning algorithm proposed in this paper is used to iteratively solve the model to obtain the completed data. Finally, the method proposed in this paper is used to complement the two situations of random loss and all-day loss of real current data in a low-voltage station area and compared with the traditional complement method. The experimental results show that this method is not only effective but also that the completion effect is better than that of other completion methods.
format	article
author	Haowen Wu Chen Yang Wenwang Xie Wei Zhang
author_facet	Haowen Wu Chen Yang Wenwang Xie Wei Zhang
author_sort	Haowen Wu
title	Joint Matrix Decomposition-Based Missing Data Completion in Low-Voltage Area
title_short	Joint Matrix Decomposition-Based Missing Data Completion in Low-Voltage Area
title_full	Joint Matrix Decomposition-Based Missing Data Completion in Low-Voltage Area
title_fullStr	Joint Matrix Decomposition-Based Missing Data Completion in Low-Voltage Area
title_full_unstemmed	Joint Matrix Decomposition-Based Missing Data Completion in Low-Voltage Area
title_sort	joint matrix decomposition-based missing data completion in low-voltage area
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/bca3a193048d4b3a818b3590cc84aee4
work_keys_str_mv	AT haowenwu jointmatrixdecompositionbasedmissingdatacompletioninlowvoltagearea AT chenyang jointmatrixdecompositionbasedmissingdatacompletioninlowvoltagearea AT wenwangxie jointmatrixdecompositionbasedmissingdatacompletioninlowvoltagearea AT weizhang jointmatrixdecompositionbasedmissingdatacompletioninlowvoltagearea
_version_	1718418303628607488

Joint Matrix Decomposition-Based Missing Data Completion in Low-Voltage Area

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