Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations

Himeur, Yassine; Alsalemi, Abdullah; Al‐Kababji, Ayman; Bensaali, Fayçal; Amira, Abbes

doi:10.1016/j.inffus.2020.07.003

Cited by 63 publications

(19 citation statements)

References 171 publications

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“…In this block, we have grouped research studies that focus on health indoors, in buildings and homes, responding to the needs of people in controlled environments [ 102 , 104 ]. Himeur [ 92 ] reviews energy efficiency systems based on data fusion from the consumer’s perspective as a cost-effective strategy for energy reduction in buildings. In terms of health, the main issue at hand, it is concluded that energy efficiency indirectly leads to improvements in terms of the environment and pollution, a benefit to public health.…”

Section: Resultsmentioning

confidence: 99%

“…In this set of research, diverse but all related to the IoT and its applications to health and sustainability, we found some works where the subject matter does not really specify the role of energy efficiency at work clearly, nor relate to any of the SDGs. For this reason, the following studies have been grouped into this block: [90,92,[94][95][96]. I. Ud Din [89] studies IoT-based smart cities, though not making special reference to energy efficiency or the SDGs.…”

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confidence: 99%

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Application of IoT in Healthcare: Keys to Implementation of the Sustainable Development Goals

Verdejo-Espinosa

Ruiz

Mata

et al. 2021

Sensors

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We live in complex times in the health, social, political, and energy spheres, and we must be aware of and implement new trends in intelligent social health systems powered by the Internet of Things (IoT). Sustainable development, energy efficiency, and public health are interrelated parameters that can transform a system or an environment for the benefit of people and the planet. The integration of sensors and smart devices should promote energy efficiency and ensure that sustainable development goals are met. This work is carried out according to a mixed approach, with a literature review and an analysis of the impact of the Sustainable Development Goals on the applications of the Internet of Things and smart systems. In the analysis of results, the following questions are answered about these systems and applications: a) Are IoT applications key to the improvement of people’s health and the environment? b) Are there research and case studies implemented in cities or territories that demonstrate the effectiveness of IoT applications and their benefits to public health? c) What sustainable development indicators and objectives can be assessed in the applications and projects analyzed?

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Section: Resultsmentioning

confidence: 99%

mentioning

confidence: 99%

Application of IoT in Healthcare: Keys to Implementation of the Sustainable Development Goals

Verdejo-Espinosa

Ruiz

Mata

et al. 2021

Sensors

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“…Furthermore, the consumption rate is predicted to be increased more in the upcoming years due to the improvement of living conditions and increasing use of appliances and electrical apparatus [16]. Overall, the conducted investigations illustrate that both the whole energy usage rate and plug loads are considerably related to the occupancy ratios [17,18]. The presence/absence of individuals in a building can highly affect energy consumption rate since a big number of users keep appliances switched on for long time without their physical presence.…”

Section: Introductionmentioning

confidence: 88%

A Novel Approach for Detecting Anomalous Energy Consumption Based on Micro-Moments and Deep Neural Networks

et al. 2020

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Nowadays, analyzing, detecting, and visualizing abnormal power consumption behavior of householders are among the principal challenges in identifying ways to reduce power consumption. This paper introduces a new solution to detect energy consumption anomalies based on extracting micro-moment features using a rule-based model. The latter is used to draw out load characteristics using daily intent-driven moments of user consumption actions. Besides micro-moment features extraction, we also experiment with a deep neural network architecture for efficient abnormality detection and classification. In the following, a novel anomaly visualization technique is introduced that is based on a scatter representation of the micro-moment classes, and hence providing consumers an easy solution to understand their abnormal behavior. Moreover, in order to validate the proposed system, a new energy consumption dataset at appliance level is also designed through a measurement campaign carried out at Qatar University Energy Lab, namely, Qatar University dataset. Experimental results on simulated and real datasets collected at two regions, which have extremely different climate conditions, confirm that the proposed deep micro-moment architecture outperforms other machine learning algorithms and can effectively detect anomalous patterns. For example, 99.58% accuracy and 97.85% F1 score have been achieved under Qatar University dataset. These promising results establish the efficacy of the proposed deep micro-moment solution for detecting abnormal energy consumption, promoting energy efficiency behaviors, and reducing wasted energy.

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“…Within this last field, several ICT solutions have been developed for collecting data and controlling building systems. Improving end-users consumption behavior has lately emerged as a cost-effective strategy for reducing wasted energy in buildings [1][2][3][4][5][6], and therefore several solutions aimed at exploring the potential introduced by various novel ICTs to design effective energy efficiency systems [6] based on raising users' energy awareness and promoting behavioural changes [7] have been recently developed. Among others, gamification has emerged as a tool for increasing residential customers' engagement in energy efficiency [8].…”

Section: Introductionmentioning

confidence: 99%

“…Among others, gamification has emerged as a tool for increasing residential customers' engagement in energy efficiency [8]. Himeur et al [6], Pasini et al [7], AlSkaif et al [8], Morganti et al [9], Johnson et al [10], Boomsma et al [11], Ouariachi et al [12], Csoknyai et al [13] and Ponce et al [14] provide an extensive review of relevant existing initiatives with various levels of success. (Figure 1).…”

Section: Introductionmentioning

confidence: 99%

Life Cycle Analysis of a Game-Based Solution for Domestic Energy Saving

et al. 2020

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ICT-based solutions are seen to be almost totally environmentally friendly, but game-based solutions for energy saving have not been explored yet. This paper describes a comprehensive analysis and an in-depth interpretation of the life cycle environmental impact of a game-based solution for domestic energy saving, developed and validated within the EU-funded Horizon 2020 project EnerGAware—Energy Game for Awareness of energy efficiency in social housing communities. Life cycle impacts were calculated with SimaPRO 8.5.2.0 using the ReCiPe 2016 v1.02 midpoint and endpoint methods and information contained within the Ecoinvent v3.4 database. Although the pre-competitive solution, directly arising from the research project, was found to have a relatively high environmental impact, its future exploitation, which mostly relies on existing infrastructure, was found to be highly competitive from an environmental perspective. The game will help reduce the life cycle impact of domestic energy consumption on damage to human health (3.68%), ecosystem quality (3.87%), and resource availability (4.81%). Most of the environmental impact of the market solution was found in the manufacturing phase (77.96–80.12%). Transport (8.86–7.57%), use (3.86–5.82%), and maintenance (7.24–7.54%) phases were found to contribute little to environmental impact. This research provides a useful reference for decision-making as it contributes to the environmental benchmarking of competing energy-saving strategies.

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Data fusion strategies for energy efficiency in buildings: Overview, challenges and novel orientations

Cited by 63 publications

References 171 publications

Application of IoT in Healthcare: Keys to Implementation of the Sustainable Development Goals

Application of IoT in Healthcare: Keys to Implementation of the Sustainable Development Goals

A Novel Approach for Detecting Anomalous Energy Consumption Based on Micro-Moments and Deep Neural Networks

Life Cycle Analysis of a Game-Based Solution for Domestic Energy Saving

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