An Intelligent Approach for Performing Energy-Driven Classification of Buildings Utilizing Joint Electricity–Gas Patterns

Building type identification is an important task that may be used in confirming and verifying its legitimate operation. One of the main sources of information over the operation of a building is its energy consumption, with the analysis of electricity patterns being at the spotlight of a non-intrus...

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Autores principales:	Cristina Nichiforov, Antonio Martinez-Molina, Miltiadis Alamaniotis
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	gas–electricity patterns building identification intelligent approach neural networks matrix profile Technology T
Acceso en línea:	https://doaj.org/article/0bbd885a4e3a4b6cb2299937b2efd61f
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spelling	oai:doaj.org-article:0bbd885a4e3a4b6cb2299937b2efd61f2021-11-25T17:25:47ZAn Intelligent Approach for Performing Energy-Driven Classification of Buildings Utilizing Joint Electricity–Gas Patterns10.3390/en142274651996-1073https://doaj.org/article/0bbd885a4e3a4b6cb2299937b2efd61f2021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/22/7465https://doaj.org/toc/1996-1073Building type identification is an important task that may be used in confirming and verifying its legitimate operation. One of the main sources of information over the operation of a building is its energy consumption, with the analysis of electricity patterns being at the spotlight of a non-intrusive identification approach. However, electricity patterns are the only source of information, and therefore, their analysis imposes several restrictions. In this work, we introduce a new approach in energy-driven identification by adding one more source of information beyond the electricity pattern that may be utilized, namely the gas consumption pattern. In particular, we propose a new intelligent approach that jointly analyzes the electricity–gas patterns to provide the type of building at hand. Our approach exploits the synergism of the matrix profile data analysis technique with a feed-forward artificial neural network. This approach has applicability in the energy waste elimination through the implementation of different energy efficiency solutions, as well as the optimization of the demand-side process management, safer and reliable operation through fault detection, and the identification and validation of the real operation of the building. The obtained results demonstrate the improvement in identifying the type of the building by employing the proposed approach for joint electricity–gas patterns as compared to only using the electricity patterns.Cristina NichiforovAntonio Martinez-MolinaMiltiadis AlamaniotisMDPI AGarticlegas–electricity patternsbuilding identificationintelligent approachneural networksmatrix profileTechnologyTENEnergies, Vol 14, Iss 7465, p 7465 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	gas–electricity patterns building identification intelligent approach neural networks matrix profile Technology T
spellingShingle	gas–electricity patterns building identification intelligent approach neural networks matrix profile Technology T Cristina Nichiforov Antonio Martinez-Molina Miltiadis Alamaniotis An Intelligent Approach for Performing Energy-Driven Classification of Buildings Utilizing Joint Electricity–Gas Patterns
description	Building type identification is an important task that may be used in confirming and verifying its legitimate operation. One of the main sources of information over the operation of a building is its energy consumption, with the analysis of electricity patterns being at the spotlight of a non-intrusive identification approach. However, electricity patterns are the only source of information, and therefore, their analysis imposes several restrictions. In this work, we introduce a new approach in energy-driven identification by adding one more source of information beyond the electricity pattern that may be utilized, namely the gas consumption pattern. In particular, we propose a new intelligent approach that jointly analyzes the electricity–gas patterns to provide the type of building at hand. Our approach exploits the synergism of the matrix profile data analysis technique with a feed-forward artificial neural network. This approach has applicability in the energy waste elimination through the implementation of different energy efficiency solutions, as well as the optimization of the demand-side process management, safer and reliable operation through fault detection, and the identification and validation of the real operation of the building. The obtained results demonstrate the improvement in identifying the type of the building by employing the proposed approach for joint electricity–gas patterns as compared to only using the electricity patterns.
format	article
author	Cristina Nichiforov Antonio Martinez-Molina Miltiadis Alamaniotis
author_facet	Cristina Nichiforov Antonio Martinez-Molina Miltiadis Alamaniotis
author_sort	Cristina Nichiforov
title	An Intelligent Approach for Performing Energy-Driven Classification of Buildings Utilizing Joint Electricity–Gas Patterns
title_short	An Intelligent Approach for Performing Energy-Driven Classification of Buildings Utilizing Joint Electricity–Gas Patterns
title_full	An Intelligent Approach for Performing Energy-Driven Classification of Buildings Utilizing Joint Electricity–Gas Patterns
title_fullStr	An Intelligent Approach for Performing Energy-Driven Classification of Buildings Utilizing Joint Electricity–Gas Patterns
title_full_unstemmed	An Intelligent Approach for Performing Energy-Driven Classification of Buildings Utilizing Joint Electricity–Gas Patterns
title_sort	intelligent approach for performing energy-driven classification of buildings utilizing joint electricity–gas patterns
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/0bbd885a4e3a4b6cb2299937b2efd61f
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An Intelligent Approach for Performing Energy-Driven Classification of Buildings Utilizing Joint Electricity–Gas Patterns

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