Real-time personalised energy saving recommendations

Sardianos, Christos; Chronis, Christos; Varlamis, Iraklis; Dimitrakopoulos, George; Himeur, Yassine; Alsalemi, Abdullah; Bensaali, Fayçal; Amira, Abbes

doi:10.1109/ithings-greencom-cpscom-smartdata-cybermatics50389.2020.00072

Cited by 18 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Several promising research directions could be pursued to mitigate the filter bubble problem: Diversity ‐ aware recommendations : Designing algorithms that aim to increase the diversity of recommendations can help in mitigating the filter bubble. These algorithms need to balance the trade‐off between relevance and diversity (Hirata et al, 2023; Sardianos, Chronis, et al, 2020). Serendipity in recommendations : Developing recommendation techniques that emphasize serendipity (unexpected but useful recommendations) could help users discover new, out‐of‐bubble content.…”

Section: Open Issues and 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

View full text Add to dashboard Cite

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

show abstract

Section: Open Issues and 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

View full text Add to dashboard Cite

show abstract

“…Specifically, this category of models helps in (i) learning a mapping to concentrate normal consumption observations in a feature space, (ii) pushing abnormal patterns to be mapped away, and (iii) providing appropriate explanations for the anomalies detected, or more exactly, a human-readable prescription presenting helpful information on the causes that have led to the anomaly. Moreover, this enables to generate tailored recommendations helping end-users in reducing their wasted energy and energy providers in detecting non-technical losses through the use of explainable recommender systems [214].…”

Section: Explainable Deep Learning Modelsmentioning

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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…To this regard, energy providers, policy makers and end-users in the building sector have become progressively aware of the importance of behavioral change in promoting energy saving and reducing carbon emissions [13,14]. In this context, an increasing number of literature, projects and commercial products have recently arisen to explore the research interest of sustainable behavior change, explicitly to address the relation between attitudes in order to improve energy consumption behavior [15]. This is also due to the widespread use of AI, Internet of things (IoT) devices and other ICT tools, which have a positive impact on raising end-users' awareness, shaping their attitudes towards energy saving and boosting their achievements [16,17].…”

Section: Introductionmentioning

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

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.

show abstract

Real-time personalised energy saving recommendations

Cited by 18 publications

References 16 publications

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

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

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

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

Contact Info

Product

Resources

About