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

dc.contributor.authorNichiforov, Cristina
dc.contributor.authorMartinez-Molina, Antonio
dc.contributor.authorAlamaniotis, Miltiadis
dc.date.accessioned2021-11-25T15:59:46Z
dc.date.available2021-11-25T15:59:46Z
dc.date.issued2021-11-09
dc.date.updated2021-11-25T15:59:47Z
dc.description.abstractBuilding 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.
dc.description.departmentElectrical and Computer Engineering
dc.description.departmentArchitecture and Planning
dc.identifierdoi: 10.3390/en14227465
dc.identifier.citationEnergies 14 (22): 7465 (2021)
dc.identifier.urihttps://hdl.handle.net/20.500.12588/745
dc.rightsAttribution 4.0 United States
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectgas–electricity patterns
dc.subjectbuilding identification
dc.subjectintelligent approach
dc.subjectneural networks
dc.subjectmatrix profile
dc.titleAn Intelligent Approach for Performing Energy-Driven Classification of Buildings Utilizing Joint Electricity–Gas Patterns
dc.typeArticle

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