A Micro-Moment System for Domestic Energy Efficiency Analysis

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

doi:10.1109/jsyst.2020.2997773

Cited by 24 publications

(12 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This can be based on the detection of historic energy consumption patterns and their underlying context, which can then be used to develop rule-based recommendations aiming to achieve end-user's satisfaction [55,56]. They usually require a longer interaction with end-users with the system in order to collect a larger amount of data that helps in a better adaptation of the generated recommendations to the specific contextual situation of the consumer [57].…”

Section: Methodologies and Algorithms For Energy Efficiency Recommend...mentioning

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

Section: Methodologies and Algorithms For Energy Efficiency Recommend...mentioning

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

“…To label QUD consumption observations, we have deployed a new platform called endorsing energy efficiency with mobile micromoment recommendations

{MathClass-open(EM MathClass-close)}^{3}

, in which micromoments of energy consumption observations are labeled using a rule‐based algorithm. In addition to the QUD subsystems that comprise sensing devices, the

{MathClass-open(EM MathClass-close)}^{3}

platform includes a secure backend that stores all the data, a micromoment classifier, and a rule‐based recommender system to produce an ensemble of tailored recommendations displayed on a mobile application 68,69 . Figure 3 illustrates the flowchart of SAD‐M2 used to detect abnormal power consumption.…”

Section: Proposed Systemmentioning

confidence: 99%

Smart power consumption abnormality detection in buildings using micromoments and improved K‐nearest neighbors

et al. 2021

Self Cite

View full text Add to dashboard Cite

Anomaly detection in energy consumption is a crucial step towards developing efficient energy saving systems, diminishing overall energy expenditure and reducing carbon emissions. Therefore, implementing powerful techniques to identify anomalous consumption in buildings and providing this information to end‐users and managers is of significant importance. Accordingly, two novel schemes are proposed in this paper; the first one is an unsupervised abnormality detection based on one‐class support vector machine, namely UAD‐OCSVM, in which abnormalities are extracted without the need of annotated data; the second is a supervised abnormality detection based on micromoments (SAD‐M2), which is implemented in the following steps: (i) normal and abnormal power consumptions are defined and assigned; (ii) a rule‐based algorithm is introduced to extract the micromoments representing the intent‐rich moments, in which the end‐users make decisions to consume energy; and (iii) an improved K‐nearest neighbors model is introduced to automatically classify consumption footprints as normal or abnormal. Empirical evaluation conducted in this framework under three different data sets demonstrates that SAD‐M2 achieves both a highest abnormality detection performance and real‐time processing capability with considerably lower computational cost in comparison with other machine learning methods. For instance, up to 99.71% accuracy and 99.77% F1 score have been achieved using a real‐world data set collected at the Qatar University energy lab.

show abstract

“…-Temperature sensors: are deployed to sense indoor temperature levels, which can be used to adjust the energy usage of heating and cooling equipment inside buildings [164,165]. -Humidity sensors: they are deployed to detect the amount of moisture and air's humidity inside buildings.…”

Section: Iot Technologies (T)mentioning

confidence: 99%

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

Himeur,

Alsalemi,

Al-Kababji

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Recently, tremendous interest has been devoted to develop data fusion strategies for energy efficiency in buildings, where various kinds of information can be processed. However, applying the appropriate data fusion strategy to design an efficient energy efficiency system is not straightforward; it requires a priori knowledge of existing fusion strategies, their applications and their properties. To this regard, seeking to provide the energy research community with a better understanding of data fusion strategies in building energy saving systems, their principles, advantages, and potential applications, this paper proposes an extensive survey of existing data fusion mechanisms deployed to reduce excessive consumption and promote sustainability. We investigate their conceptualizations, advantages, challenges and drawbacks, as well as performing a taxonomy of existing data fusion strategies and other contributing factors. Following, a comprehensive comparison of the state-of-theart data fusion based energy efficiency frameworks is conducted using various parameters, including data fusion level, data fusion techniques, behavioral change influencer, behavioral change incentive, recorded data, platform architecture, IoT technology and application scenario. Moreover, a novel method for electrical appliance identification is proposed based on the fusion of 2D local texture descriptors, where 1D power signals are transformed into 2D space and treated as images. The empirical evaluation, conducted on three real datasets, shows promising performance, in which up to 99.68% accuracy and 99.52% F1 score have been attained. In addition, various open research challenges and future orientations to improve data fusion based energy efficiency ecosystems are explored.Keywords Data fusion • energy efficiency • sensors • appliance identification • fusion of 2D descriptors • machine learning.

show abstract

A Micro-Moment System for Domestic Energy Efficiency Analysis

Cited by 24 publications

References 19 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

Smart power consumption abnormality detection in buildings using micromoments and improved K‐nearest neighbors

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

Contact Info

Product

Resources

About