An innovative deep anomaly detection of building energy consumption using energy time-series images

Copiaco, Abigail; Himeur, Yassine; Amira, Abbes; Mansoor, Wathiq; Fadli, Fodil; Atalla, Shadi; Sohail, Shahab Saquib

doi:10.1016/j.engappai.2022.105775

Cited by 70 publications

(27 citation statements)

References 69 publications

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“…Among them, unsupervised pattern recognition is the most widely applied approach to identify rarely occurring behaviors that differ from frequently observed patterns. Typical methods include clustering, one-class learning, and autoencoders. − Another type of technique is the prediction-based method whose aim is to identify anomalies through classification or by comparing the predicted value with the actual value. ,, Furthermore, hybrid techniques, such as the semisupervised approach, have also been explored. − These methods are usually constructed based on historical building energy data, and some of them have also considered contextual information, such as season and day of the week, to identify anomalies in a specific context. ,,− …”

Section: Where and How Data Science Has Helped The Circular Economy?mentioning

confidence: 99%

Data Science Applications in Circular Economy: Trends, Status, and Future

Zhao,

Yu,

Wang

et al. 2024

Environ. Sci. Technol.

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The circular economy (CE) aims to decouple the growth of the economy from the consumption of finite resources through strategies, such as eliminating waste, circulating materials in use, and regenerating natural systems. Due to the rapid development of data science (DS), promising progress has been made in the transition toward CE in the past decade. DS offers various methods to achieve accurate predictions, accelerate product sustainable design, prolong asset life, optimize the infrastructure needed to circulate materials, and provide evidence-based insights. Despite the exciting scientific advances in this field, there still lacks a comprehensive review on this topic to summarize past achievements, synthesize knowledge gained, and navigate future research directions. In this paper, we try to summarize how DS accelerated the transition to CE. We conducted a critical review of where and how DS has helped the CE transition with a focus on four areas including (1) characterizing socioeconomic metabolism, (2) reducing unnecessary waste generation by enhancing material efficiency and optimizing product design, (3) extending product lifetime through repair, and (4) facilitating waste reuse and recycling. We also introduced the limitations and challenges in the current applications and discussed opportunities to provide a clear roadmap for future research in this field.

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Section: Where and How Data Science Has Helped The Circular Economy?mentioning

confidence: 99%

Data Science Applications in Circular Economy: Trends, Status, and Future

Zhao,

Yu,

Wang

et al. 2024

Environ. Sci. Technol.

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“…3. An innovative deep anomaly detection of building energy consumption using energy time-series images (Copiaco et al, 2023): This study proposed a new approach to anomaly detection in building energy consumption by converting energy time-series data into images and applying deep learning techniques. The authors demonstrated that their method achieved higher anomaly detection accuracy than traditional anomaly detection methods.…”

Section: Random Forest Modelmentioning

confidence: 99%

Reducing carbon emissions in the architectural design process via transformer with cross-attention mechanism

Li,

Yang,

Zhu

2023

Front. Ecol. Evol.

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IntroductionThe construction industry is one of the world’s largest carbon emitters, accounting for around 40% of total emissions. Therefore, reducing carbon emissions from the construction sector is critical to global climate change mitigation. However, traditional architectural design methods have some limitations, such as difficulty in considering complex interaction relationships and a large amount of architectural data, so machine learning can assist architectural design in improving design efficiency and reducing carbon emissions.MethodsThis study aims to reduce carbon emissions in the architectural design by using a Transformer with a cross-attention mechanism model. We aim to use machine learning methods to generate optimized building designs that reduce carbon emissions during their use and construction. We train the model on the building design dataset and its associated carbon emissions dataset and use a cross-attention mechanism to let the model focus on different aspects of the building design to achieve the desired outcome. We also use predictive modelling to predict energy consumption and carbon emissions to help architects make more sustainable decisions.Results and discussionExperimental results demonstrate that our model can generate optimized building designs to reduce carbon emissions during their use and construction. Our model can also predict a building’s energy consumption and carbon emissions, helping architects make more sustainable decisions. Using Transformers with cross-attention mechanism models to reduce carbon emissions in the building design process can contribute to climate change mitigation. This approach could help architects better account for carbon emissions and energy consumption and produce more sustainable building designs. In addition, the method can also guide future building design and decision-making by predicting building energy consumption and carbon emissions.

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“…Most unsupervised anomaly detection techniques such as clustering, one-class classification, and dimensionality reduction, such as single-class SVM (OCSVM) algorithms, can only detect one type of energy usage anomaly, and in the smart building domain, these univariate time-series anomaly detection methods only analyze energy consumption data without considering other relevant factors that affect energy usage [ 6 ]. Among them, Copiaco A et al [ 16 ] did the most exhaustive work by analyzing building energy data for anomaly detection. Their innovation in using energy time series images to detect energy consumption anomalies was done by converting 1D energy time series into 2D images, using pre-trained convolutional neural network (CNN) models as feature extractors, and using support vector machines (SVM) to accomplish anomaly classification.…”

Section: Introductionmentioning

confidence: 99%

Anomaly detection method for building energy consumption in multivariate time series based on graph attention mechanism

et al. 2023

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A critical issue in intelligent building control is detecting energy consumption anomalies based on intelligent device status data. The building field is plagued by energy consumption anomalies caused by a number of factors, many of which are associated with one another in apparent temporal relationships. For the detection of abnormalities, most traditional detection methods rely solely on a single variable of energy consumption data and its time series changes. Therefore, they are unable to examine the correlation between the multiple characteristic factors that affect energy consumption anomalies and their relationship in time. The outcomes of anomaly detection are one-sided. To address the above problems, this paper proposes an anomaly detection method based on multivariate time series. Firstly, in order to extract the correlation between different feature variables affecting energy consumption, this paper introduces a graph convolutional network to build an anomaly detection framework. Secondly, as different feature variables have different influences on each other, the framework is enhanced by a graph attention mechanism so that time series features with higher influence on energy consumption are given more attention weights, resulting in better anomaly detection of building energy consumption. Finally, the effectiveness of this paper’s method and existing methods for detecting energy consumption anomalies in smart buildings are compared using standard data sets. The experimental results show that the model has better detection accuracy.

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An innovative deep anomaly detection of building energy consumption using energy time-series images

Cited by 70 publications

References 69 publications

Data Science Applications in Circular Economy: Trends, Status, and Future

Data Science Applications in Circular Economy: Trends, Status, and Future

Reducing carbon emissions in the architectural design process via transformer with cross-attention mechanism

Anomaly detection method for building energy consumption in multivariate time series based on graph attention mechanism

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