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

Cristina Nichiforov, Antonio Martinez-Molina and Miltiadis Alamaniotis
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Cristina Nichiforov: Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX 78207, USA
Antonio Martinez-Molina: School of Architecture and Planning, University of Texas at San Antonio, San Antonio, TX 78207, USA
Miltiadis Alamaniotis: Department of Electrical and Computer Engineering, University of Texas at San Antonio, San Antonio, TX 78207, USA

Energies, 2021, vol. 14, issue 22, 1-11

Abstract: 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.

Keywords: gas–electricity patterns; building identification; intelligent approach; neural networks; matrix profile (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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