Comparative analysis of select techniques and metrics for data reconciliation in smart energy distribution network

Reliability of each state of process in many chemical process industries largely relies upon water and vitality supplies. In this way, there is great necessity to have an improved and controlled smart energy distribution network (SEDN) in industries. In SEDNs, sensor information related to flow cont...

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Autores principales:	Jeyanthi Ramasamy, Sriram Devanathan, Dhanalakshmi Jayaraman
Formato:	article
Lenguaje:	EN
Publicado:	IWA Publishing 2021
Materias:	data reconciliation performance metrics principal component analysis (pca) smart energy management network Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
Acceso en línea:	https://doaj.org/article/a218135b79194418b5600af57d32925b
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spelling	oai:doaj.org-article:a218135b79194418b5600af57d32925b2021-11-06T07:18:33ZComparative analysis of select techniques and metrics for data reconciliation in smart energy distribution network1606-97491607-079810.2166/ws.2020.314https://doaj.org/article/a218135b79194418b5600af57d32925b2021-08-01T00:00:00Zhttp://ws.iwaponline.com/content/21/5/2109https://doaj.org/toc/1606-9749https://doaj.org/toc/1607-0798Reliability of each state of process in many chemical process industries largely relies upon water and vitality supplies. In this way, there is great necessity to have an improved and controlled smart energy distribution network (SEDN) in industries. In SEDNs, sensor information related to flow control and optimization serves as a basis for modelling of energy management systems. Therefore, it is important to ensure that sensor data are accurate and precise. However, they are affected by random noise and measurement biases, which compromise the quality of measurements. Data Reconciliation (DR) is one such approach popularly used in industries to reduce the adverse impact of random errors present in pipe flow measurements. In this study, Python-based simulations of weighted least squares (WLS) and principal component analysis (PCA) based DR techniques are implemented on the selected flow streams of SEDN, and reconciled estimates are obtained. The results show that Root Mean Square Error (RMSE) is the best performance metric since it is more sensitive to small changes in the measurement values and the reconciled estimates. Further, it is observed that PCA-DR performs better than WLS-DR in reducing the random error (and thereby achieving greater precision of measured values). HIGHLIGHTS Application of data reconciliation (DR) techniques to treat random errors present in flow sensor data used by water distribution networks.; Selection of best performing metric to evaluate data reconciliation (DR) techniques.; Analyze the performance of selected DR techniques for small and large scale networks using Python-based simulation.;Jeyanthi RamasamySriram DevanathanDhanalakshmi JayaramanIWA Publishingarticledata reconciliationperformance metricsprincipal component analysis (pca)smart energy management networkWater supply for domestic and industrial purposesTD201-500River, lake, and water-supply engineering (General)TC401-506ENWater Supply, Vol 21, Iss 5, Pp 2109-2121 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	data reconciliation performance metrics principal component analysis (pca) smart energy management network Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506
spellingShingle	data reconciliation performance metrics principal component analysis (pca) smart energy management network Water supply for domestic and industrial purposes TD201-500 River, lake, and water-supply engineering (General) TC401-506 Jeyanthi Ramasamy Sriram Devanathan Dhanalakshmi Jayaraman Comparative analysis of select techniques and metrics for data reconciliation in smart energy distribution network
description	Reliability of each state of process in many chemical process industries largely relies upon water and vitality supplies. In this way, there is great necessity to have an improved and controlled smart energy distribution network (SEDN) in industries. In SEDNs, sensor information related to flow control and optimization serves as a basis for modelling of energy management systems. Therefore, it is important to ensure that sensor data are accurate and precise. However, they are affected by random noise and measurement biases, which compromise the quality of measurements. Data Reconciliation (DR) is one such approach popularly used in industries to reduce the adverse impact of random errors present in pipe flow measurements. In this study, Python-based simulations of weighted least squares (WLS) and principal component analysis (PCA) based DR techniques are implemented on the selected flow streams of SEDN, and reconciled estimates are obtained. The results show that Root Mean Square Error (RMSE) is the best performance metric since it is more sensitive to small changes in the measurement values and the reconciled estimates. Further, it is observed that PCA-DR performs better than WLS-DR in reducing the random error (and thereby achieving greater precision of measured values). HIGHLIGHTS Application of data reconciliation (DR) techniques to treat random errors present in flow sensor data used by water distribution networks.; Selection of best performing metric to evaluate data reconciliation (DR) techniques.; Analyze the performance of selected DR techniques for small and large scale networks using Python-based simulation.;
format	article
author	Jeyanthi Ramasamy Sriram Devanathan Dhanalakshmi Jayaraman
author_facet	Jeyanthi Ramasamy Sriram Devanathan Dhanalakshmi Jayaraman
author_sort	Jeyanthi Ramasamy
title	Comparative analysis of select techniques and metrics for data reconciliation in smart energy distribution network
title_short	Comparative analysis of select techniques and metrics for data reconciliation in smart energy distribution network
title_full	Comparative analysis of select techniques and metrics for data reconciliation in smart energy distribution network
title_fullStr	Comparative analysis of select techniques and metrics for data reconciliation in smart energy distribution network
title_full_unstemmed	Comparative analysis of select techniques and metrics for data reconciliation in smart energy distribution network
title_sort	comparative analysis of select techniques and metrics for data reconciliation in smart energy distribution network
publisher	IWA Publishing
publishDate	2021
url	https://doaj.org/article/a218135b79194418b5600af57d32925b
work_keys_str_mv	AT jeyanthiramasamy comparativeanalysisofselecttechniquesandmetricsfordatareconciliationinsmartenergydistributionnetwork AT sriramdevanathan comparativeanalysisofselecttechniquesandmetricsfordatareconciliationinsmartenergydistributionnetwork AT dhanalakshmijayaraman comparativeanalysisofselecttechniquesandmetricsfordatareconciliationinsmartenergydistributionnetwork
_version_	1718443813447401472

Comparative analysis of select techniques and metrics for data reconciliation in smart energy distribution network

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