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.
Highlights
Published: 9 November 2021The building energy sector has recently attracted a lot of research attention boosted by new capabilities offered by the advances in data science and artificial intelligence
Model outperforms the Artificial Neural Network (ANN)-E model. An interpretation of this observation states that the use of both discord-based load and gas patterns offers more insightful information about the primary operation of each building compared to the ANN-E model
This work proposes a novel approach based on the combination of the Matrix Profile data mining technique and the Artificial Neural Network model
Summary
The building energy sector has recently attracted a lot of research attention boosted by new capabilities offered by the advances in data science and artificial intelligence. The authors have demonstrated that the unsupervised methods are efficient at the extraction and identification of operation patterns and faults in complex building energy systems They further proved that the AI supervised data mining-based methods perform satisfactorily in tasks related to building energy demand anticipation, fault detection, and diagnosis. (i) Section 2 discusses the proposed two-dimensional approach for building identification and the Matrix Profile data mining technique used for extracting the anomalies found in the load and gas patterns with the aim of identifying the primary usage of buildings through two proposed ANN classification models; (ii) Section 3 presents relevant experimental results on building identification based on using the top 10 anomalies found in both the gas and load patterns; and (iii) Section 4 concludes the paper and lists directions for future work The rest of the paper is structured as follows. (i) Section 2 discusses the proposed two-dimensional approach for building identification and the Matrix Profile data mining technique used for extracting the anomalies found in the load and gas patterns with the aim of identifying the primary usage of buildings through two proposed ANN classification models; (ii) Section 3 presents relevant experimental results on building identification based on using the top 10 anomalies found in both the gas and load patterns; and (iii) Section 4 concludes the paper and lists directions for future work
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