Recommendation System Towards Residential Energy Saving Based on Anomaly Detection

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

doi:10.1109/icspis57063.2022.10002437

Cited by 4 publications

(5 citation statements)

References 18 publications

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“…the trainable parameter of this transformation. The original attention score between each node is computed by Eq (11), which first splices two node embeddings, followed by a dot product of the spliced embeddings and a learnable weight vector; finally, a LeakyReLU activation function is applied. This form of attention mechanism is often referred to as "additive attention" and is distinct from the "dot product" attention in Transformer.…”

Section: Plos Onementioning

confidence: 99%

“…Use GAT to calculate the weight values between each feature node; take the feature factors L 1 and L 2 as an example, as shown in Fig 7(B). GAT linearly transforms the two nodes separately, and the original attention coefficient e 12 between each node is calculated by Eqs ( 10) and (11), then the attention coefficient e 12 is assigned to the nodes in the graph using the mapping function, and in order to compare the attention coefficients between different nodes, the attention weight b is obtained by normalizing using the softmax function. W!…”

Section: Plos Onementioning

confidence: 99%

“…lei L et al [9] proposed a building energy consumption data dynamic anomaly detection algorithm (BECMP), which implements dynamic detection of point anomalies and collective anomalies. Shrestha R et al [10] proposed a long short-term neural network (LSTM) based anomaly detection (ADLA-FED) which has a framework for implementing auto encoder in smart grid.A suitable method of detecting building energy anomalies helps to minimize the power consumption of the home while maintaining the comfort of the end user, Atalla S et al [11] proposed a system architecture for improving energy efficiency in residential buildings to promote energy reduction behavior in residential buildings.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Plos Onementioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2023

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“… Explainability and transparency : Explainable AI can help users understand why a particular recommendation was made. Seeing the rationale behind the recommendations might make users more receptive to different content, reducing the filter bubble effect (Atalla et al, 2022). User ‐ controlled recommendations : Allowing users to have more control over their recommendations, such as adjusting the degree of novelty or diversity, could also help alleviate the filter bubble problem. Cross ‐ domain recommendations : Leveraging data from different domains can help in providing a broader range of recommendations.…”

Section: Open Issues and Future Research Directionsmentioning

confidence: 99%

“…• Explainability and transparency: Explainable AI can help users understand why a particular recommendation was made. Seeing the rationale behind the recommendations might make users more receptive to different content, reducing the filter bubble effect (Atalla et al, 2022).…”

Section: Future Research Directionsmentioning

confidence: 99%

Filter bubbles in recommender systems: Fact or fallacy—A systematic review

Areeb

Nadeem

Sohail

et al. 2023

WIREs Data Min & Knowl

Self Cite

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A filter bubble refers to the phenomenon where Internet customization effectively isolates individuals from diverse opinions or materials, resulting in their exposure to only a select set of content. This can lead to the reinforcement of existing attitudes, beliefs, or conditions. In this study, our primary focus is to investigate the impact of filter bubbles in recommender systems (RSs). This pioneering research aims to uncover the reasons behind this problem, explore potential solutions, and propose an integrated tool to help users avoid filter bubbles in RSs. To achieve this objective, we conduct a systematic literature review on the topic of filter bubbles in RSs. The reviewed articles are carefully analyzed and classified, providing valuable insights that inform the development of an integrated approach. Notably, our review reveals evidence of filter bubbles in RSs, highlighting several biases that contribute to their existence. Moreover, we propose mechanisms to mitigate the impact of filter bubbles and demonstrate that incorporating diversity into recommendations can potentially help alleviate this issue. The findings of this timely review will serve as a benchmark for researchers working in interdisciplinary fields such as privacy, artificial intelligence ethics, and RSs. Furthermore, it will open new avenues for future research in related domains, prompting further exploration and advancement in this critical area.This article is categorized under: Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction Application Areas > Internet Commercial, Legal, and Ethical Issues > Ethical Considerations Commercial, Legal, and Ethical Issues > Security and Privacy

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Exploring Deep Time-Series Imaging for Anomaly Detection of Building Energy Consumption

Copiaco

Himeur

Amira

et al. 2022

2022 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE)

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Recommendation System Towards Residential Energy Saving Based on Anomaly Detection

Cited by 4 publications

References 18 publications

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

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

Filter bubbles in recommender systems: Fact or fallacy—A systematic review

Exploring Deep Time-Series Imaging for Anomaly Detection of Building Energy Consumption

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