Energy Data Visualizations on Smartphones for Triggering Behavioral Change: Novel Vs. Conventional

Al‐Kababji, Ayman; Alsalemi, Abdullah; Himeur, Yassine; Bensaali, Fayçal; Amira, Abbes; Fernandez, Rachael; Fetais, Noora

doi:10.1109/gpecom49333.2020.9247901

Cited by 17 publications

(11 citation statements)

References 7 publications

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“…CASER framework produces both web and mobile applications for data visualization but showcase variant visualization including timeseries and heatmap charts on both household and substation levels (multiple households) [221]. Lastly, (EM) 3 creates two distinct applications, where the first application on iOS showcases data visualization in recommendations [222] and the latter on both Android and iOS, developed on React Native, studies the effect of different charts on end-users understanding [223]. Fig.…”

Section: Visualization Recommender Systemsmentioning

confidence: 99%

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

Himeur¹,

Alsalemi²,

Al‐Kababji³

et al. 2021

Preprint

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Recommender systems have significantly developed in recent years in parallel with the witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI, multiple forms of data are incorporated in these systems, e.g. social, implicit, local and personal information, which can help in improving recommender systems' performance and widen their applicability to traverse different disciplines. On the other side, energy efficiency in the building sector is becoming a hot research topic, in which recommender systems play a major role by promoting energy saving behavior and reducing carbon emissions. However, the deployment of the recommendation frameworks in buildings still needs more investigations to identify the current challenges and issues, where their solutions are the keys to enable the pervasiveness of research findings, and therefore, ensure a large-scale adoption of this technology. Accordingly, this paper presents, to the best of the authors' knowledge, the first timely and comprehensive reference for energy-efficiency recommendation systems through (i) surveying existing recommender systems for energy saving in buildings; (ii) discussing their evolution; (iii) providing an original taxonomy of these systems based on specified criteria, including the nature of the recommender engine, its objective, computing platforms, evaluation metrics and incentive measures; and (iv) conducting an in-depth, critical analysis to identify their limitations and unsolved issues. The derived challenges and areas of future implementation could effectively guide the energy research community to improve the energy-efficiency in buildings and reduce the cost of developed recommender systems-based solutions.

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Section: Visualization Recommender Systemsmentioning

confidence: 99%

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

Himeur¹,

Alsalemi²,

Al‐Kababji³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The algorithm is based on the extracted user's habits that concern the repeated usage of devices at certain moments during the day [32]. It is extracted from the energy consumption data and the room occupancy information recorded in users' (or office) recent history of activities [33]. Fig.…”

Section: Appliance-levelmentioning

confidence: 99%

Cloud Energy Micro-Moment Data Classification: A Platform Study

Alsalemi

Al‐Kababji

Himeur

et al. 2020

2020 IEEE/ACM 13th International Conference on Utility and Cloud Computing (UCC)

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Energy efficiency is a crucial factor in the wellbeing of our planet. In parallel, Machine Learning (ML) plays an instrumental role in automating our lives and creating convenient workflows for enhancing behavior. So, analyzing energy behavior can help understand weak points and lay the path towards better interventions. Moving towards higher performance, cloud platforms can assist researchers in conducting classification trials that need high computational power. Under the larger umbrella of the Consumer Engagement Towards Energy Saving Behavior by means of Exploiting Micro Moments and Mobile Recommendation Systems (EM) 3 framework, we aim to influence consumers' behavioral change via improving their power consumption consciousness. In this paper, common cloud artificial intelligence platforms are benchmarked and compared for micromoment classification. Amazon Web Services, Google Cloud Platform, Google Colab, and Microsoft Azure Machine Learning are employed on simulated and real energy consumption datasets. The KNN, DNN, and SVM classifiers have been employed. Superb performance has been observed in the selected cloud platforms, showing relatively close performance. Yet, the nature of some algorithms limits the training performance.

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“…televisions from distinct brands). Visualizing multi-level power consumption could help end-users in effectively detecting anomalies and faulty devices, and hence could allow them to perform better decision-making towards reducing wasted energy [227]. Fig.…”

Section: Innovative Anomaly Visualizationmentioning

confidence: 99%

Artificial Intelligence based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives

Himeur,

Ghanem,

Alsalemi

et al. 2020

Preprint

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Enormous amounts of data are being produced everyday by submeters and smart sensors installed in different kinds of buildings. If leveraged properly, that data could assist end-users, energy producers and utility companies in detecting anomalous power consumption and understanding the causes of each anomaly. Therefore, anomaly detection could stop a minor problem to become widespread, costly and time-consuming issue. Moreover, this will help in better decision-making to reduce wasted energy and promote sustainable and energy efficiency behavior. In this regard, this paper is proposed to indepthly review existing frameworks of anomaly detection in power consumption and provide a critical analysis of existing solutions. Specifically, a comprehensive survey is introduced, in which a novel taxonomy is introduced to classify existing algorithms based on different factors adopted in their implementation, such as the machine learning algorithm, feature extraction approach, detection level, computing platform, application scenario and privacy preservation. To the best of the authors' knowledge, this is the first review article that discusses the anomaly detection in building energy consumption. Moving forward, important findings along with domain-specific problems, difficulties and challenges that remain unresolved are thoroughly discussed, including the absence of: (i) precise definitions of anomalous power consumptions, (ii) annotated datasets, (iii) unified metrics to assess the performance of existing solutions, and (iv) platforms for reproducibility. Following, insights about current research trends that anomaly detection technology needs to target for widespreading its application and facilitate its implementation are described before deriving a set of challenging future directions attracting significant research and development attention.

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Energy Data Visualizations on Smartphones for Triggering Behavioral Change: Novel Vs. Conventional

Cited by 17 publications

References 7 publications

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

Cloud Energy Micro-Moment Data Classification: A Platform Study

Artificial Intelligence based Anomaly Detection of Energy Consumption in Buildings: A Review, Current Trends and New Perspectives

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