Smart fusion of sensor data and human feedback for personalized energy-saving recommendations

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

doi:10.1016/j.apenergy.2021.117775

Cited by 36 publications

(6 citation statements)

References 37 publications

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“…In recent times, there has been a growing interest in explainable artificial intelligence (XAI) across various research domains, aiming to address the challenges posed by increasing complexity, scalability, and automation (Arrieta et al, 2020; Varlamis et al, 2022). Consequently, the development of XRSs has gained momentum.…”

Section: Preventing Filter Bubblementioning

confidence: 99%

“…The proliferation of the Internet has resulted in an overwhelming abundance of information, necessitating the development of systems that can curate and present tailored options from the vast array of available resources (Atalla et al, 2023; Sayed et al, 2021). Recommender systems (RSs) have emerged as a prominent research area, rapidly advancing in their ability to provide users with personalized recommendations for items of interest (Himeur et al, 2021; Varlamis et al, 2022). However, as the field of RSs progresses, several critical issues have been identified in the literature (Dokoupil, 2022; Sayed et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Areeb

Nadeem

Sohail

et al. 2023

WIREs Data Min & Knowl

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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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Section: Preventing Filter Bubblementioning

confidence: 99%

Section: Introductionmentioning

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

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“…2 Consequently, developing robust solutions to offer end-users this information and motivate them to endorse more sustainable energy behaviors is becoming a hot research topic. 3 To that end, a straightforward strategy to achieve that objective is through adopting "intrusive load monitoring (ILM)," which involves the installation of individual smart meters/sensors at every single appliance in a building, which unfortunately comes with a high cost. Accordingly, another promising solution comes into play, namely, "nonintrusive load monitoring (NILM)," which can segregate energy consumption footprints of various appliances using only one recording point.…”

Section: Introductionmentioning

confidence: 99%

“…For example, power consumption in residential households could be facilely decreased by more than 10% via providing end‐users with their energy usage feedback; precisely, the way they consume their energy and their domestic devices' energy consumption throughout the day 2 . Consequently, developing robust solutions to offer end‐users this information and motivate them to endorse more sustainable energy behaviors is becoming a hot research topic 3 . To that end, a straightforward strategy to achieve that objective is through adopting “intrusive load monitoring (ILM),” which involves the installation of individual smart meters/sensors at every single appliance in a building, which unfortunately comes with a high cost.…”

Section: Introductionmentioning

confidence: 99%

Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions

Himeur

Alsalemi

Bensaali

et al. 2022

Int J of Intelligent Sys

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Smart nonintrusive load monitoring (NILM) represents a cost-efficient technology for observing power usage in buildings. It tackles several challenges in transitioning into a more effective, sustainable, and digital energy efficiency environment. This paper presents a comprehensive review of recent trends in the NILM field, in which we propose a multiperspective classification of existing smart NILM techniques. More attention is devoted to describing the contributions of deep learning, feature extraction, computing platforms, and application scenarios for NILM development. Accordingly, NILM technical aspects are first Int J Intell Syst. 2022;37:7124-7179. wileyonlinelibrary.com/journal/int investigated, including data collection devices and public data sets. Next, event-based and non-eventbased NILM algorithms are overviewed. Furthermore, potential limitations of existing solutions are identified, highlighting their technical challenges, especially those related to security and privacy preservation, data scarcity, results reproduction, and implementation and business difficulties. Lastly, future directions are explored to overcome the identified limitations.

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“…On the other hand, the recommendation systems could support occupants in evaluating their activities regarding energy consumption and comfort. Their past activities could be analyzed to suggest better practices [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Dynamic Segmentation of Sensor Events for Real-Time Human Activity Recognition in a Smart Home Context

Najeh

Lohr

Leduc³

2022

Sensors

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Human activity recognition (HAR) is fundamental to many services in smart buildings. However, providing sufficiently robust activity recognition systems that could be confidently deployed in an ordinary real environment remains a major challenge. Much of the research done in this area has mainly focused on recognition through pre-segmented sensor data. In this paper, real-time human activity recognition based on streaming sensors is investigated. The proposed methodology incorporates dynamic event windowing based on spatio-temporal correlation and the knowledge of activity trigger sensor to recognize activities and record new events. The objective is to determine whether the last event that just happened belongs to the current activity, or if it is the sign of the start of a new activity. For this, we consider the correlation between sensors in view of what can be seen in the history of past events. The proposed algorithm contains three steps: verification of sensor correlation (SC), verification of temporal correlation (TC), and determination of the activity triggering the sensor. The proposed approach is applied to a real case study: the “Aruba” dataset from the CASAS database. F1 score is used to assess the quality of the segmentation. The results show that the proposed approach segments several activities (sleeping, bed to toilet, meal preparation, eating, housekeeping, working, entering home, and leaving home) with an F1 score of 0.63–0.99.

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Smart fusion of sensor data and human feedback for personalized energy-saving recommendations

Cited by 36 publications

References 37 publications

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

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

Recent trends of smart nonintrusive load monitoring in buildings: A review, open challenges, and future directions

Dynamic Segmentation of Sensor Events for Real-Time Human Activity Recognition in a Smart Home Context

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