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...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Cristina Nichiforov, Antonio Martinez-Molina, Miltiadis Alamaniotis
Formato: article
Lenguaje:EN
Publicado: MDPI AG 2021
Materias:
T
Acceso en línea:https://doaj.org/article/0bbd885a4e3a4b6cb2299937b2efd61f
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
Descripción
Sumario: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.