Abdelkareem M Jaradat

Abdelkareem M Jaradat

Western University

H-index: 3

North America-Canada

About Abdelkareem M Jaradat

Abdelkareem M Jaradat, With an exceptional h-index of 3 and a recent h-index of 3 (since 2020), a distinguished researcher at Western University, specializes in the field of Smart Systems, SHEMSs, Machine Learning.

Abdelkareem M Jaradat Information

University

Western University

Position

PhD Student

Citations(all)

14

Citations(since 2020)

14

Cited By

1

hIndex(all)

3

hIndex(since 2020)

3

i10Index(all)

0

i10Index(since 2020)

0

Email

University Profile Page

Western University

Abdelkareem M Jaradat Skills & Research Interests

Smart Systems

SHEMSs

Machine Learning

Top articles of Abdelkareem M Jaradat

SCOPE: Smart Cooperative Parking Environment

The shortage of parking spaces in metropolitan cities has become a significant challenge, leading to wasted time, money, traffic congestion, and environmental pollution. While smart parking solutions offer potential relief, existing systems often struggle with integration and coordination issues in the complex smart city ecosystem. In response, this paper introduces SCOPE, a cooperative distributed system architecture and interaction model that facilitates the management of parking spaces in a smart city through coordination and autonomous interactions. The system leverages an overlay network, a hierarchical and spatial structure of coordination nodes, and an integration layer to organize traffic and communication among facilities. By incorporating a sharing economy business model, SCOPE maximizes parking resource usage, merges public and private parking resources, and provides economic opportunities for …

Authors

Muhamed Alarbi,Abdelkareem Jaradat,Hanan Lutfiyya,Anwar Haque

Journal

IEEE Access

Published Date

2023/10/27

Appliances Operation Modes Identification Using States Clustering

The increasing cost, energy demand, and environmental issues have led many researchers to find approaches for energy monitoring, and hence energy conservation. The emerging technologies of the Internet of Things (IoT) and Machine Learning (ML) deliver techniques that have the potential to conserve energy and improve the utilization of energy consumption efficiently. Smart Home Energy Management Systems (SHEMSs) have the potential to contribute to energy conservation through the application of Demand Response (DR) in the residential sector. In this paper, the aPpliances opeRation mOdes idenTification using statEs ClusTering (PROTECT) is proposed, a SHEMS analytical component that utilizes the sensed residential disaggregated power consumption in supporting DR by providing consumers with the opportunity to select lighter Appliance Operation Modes (AOMs). The states of an appliance’s …

Authors

Abdelkareem Jaradat,Muhamed Alarbi,Hanan Lutfiyya,Anwar Haque

Published Date

2023/7/25

Density And Dynamic Time Warping Based Spatial Clustering For Appliance Operation Modes

Household demand response (DR) is an important research problem that aims to modify consumer’s energy consumption. One of the promising areas is clustering Appliance Operation Modes (AOMs) and inducing DR by promoting consumption patterns that use less energy-intensive modes. This work proposes a novel clustering approach (DDTWSC) which aims to cluster AOMs based on the similarity of the appliance load profiles (SUPs). DDTWSC leverages the power of the DensityBased Spatial Clustering of Applications with Noise (DBSCAN) algorithm to partition the appliance load profiles into clusters of similar profiles that share the same AOM. Within DBSCAN, to measure the similarity between SUPs, the Dynamic Time Warping (DTW) algorithm is used. The resulting clustering is evaluated against two publicly datasets, namely RAE and UK-DALE. The Silhouette score is used to measure the performance of …

Authors

Abdelkareem Jaradat,Hanan Lutfiyya,Anwar Haque

Published Date

2023/3/12

Smart home energy visualizer: a fusion of data analytics and information visualization

While technology advancements are continuously improving, the energy efficiency of household appliances, energy consumption analysis, and providing feedback to consumers on this analysis remains a critical issue in ensuring the effectiveness of such improvements. Visual feedback is a promising technique for promoting energy conservation by applying demand response (DR) in smart home energy management systems (SHEMSs). In this article, we propose a smart home energy visualization (SHEV) system, an SHEMS that comprises three components: Appliance Profile Detector with XCorrelation (APDX) that monitors the activation of household appliances, operation modes identification using cycles clustering (OMICC) to identify the operation modes used, and the visualizer to represent the appliance usage-related information to the user using concentric circles representation (CCR). This visualization …

Authors

Abdelkareem Jaradat,Hanan Lutfiyya,Anwar Haque

Journal

IEEE Canadian Journal of Electrical and Computer Engineering

Published Date

2022/1/5

Appliance operation modes identification using cycles clustering

The increasing cost, energy demand, and environmental issues has led many researchers to find approaches for energy monitoring, and hence energy conservation. The emerging technologies of Internet of Things (IoT) and Machine Learning (ML) deliver techniques that have the potential to efficiently conserve energy and improve the utilization of energy consumption. Smart Home Energy Management Systems (SHEMSs) have the potential to contribute in energy conservation through the application of Demand Response (DR) in the residential sector. In this paper, we propose appliances Operation Modes Identification using Cycles Clustering (OMICC) which is SHEMS fundamental approach that utilizes the sensed residential disaggregated power consumption in supporting DR by providing consumers the opportunity to select lighter appliance operation modes. The cycles of the Single Usage Profile (SUP) of an appliance are extracted and reformed into features in terms of clusters of cycles. These features are then used to identify the operation mode used in every occurrence using K-Nearest Neighbors (KNN). Operation modes identification is considered a basis for many potential smart DR applications within SHEMS towards the consumers or the suppliers

Authors

Abdelkareem Jaradat,Hanan Lutfiyya,Anwar Haque

Journal

arXiv preprint arXiv:2101.10472

Published Date

2021/1/25

Demand response for residential uses: A data analytics approach

In the Smart Grid environment, the advent of intelligent measuring devices facilitates monitoring appliance electricity consumption. This data can be used in applying Demand Response (DR) in residential houses through data analytics, and developing data mining techniques. In this research, we introduce a smart system foundation that is applied to user’s disaggregated power consumption data. This system encourages the users to apply DR by changing their behaviour of using heavier operation modes to lighter modes, and by encouraging users to shift their usages to off-peak hours. First, we apply Cross Correlation (XCORR) to detect times of the occurrences when an appliance is being used. We then use The Dynamic Time Warping (DTW) [14] algorithm to recognize the operation mode used.

Authors

Abdelkareem Jaradat,Hanan Lutfiyya,Anwar Haque

Published Date

2020/6/2

Abdelkareem M Jaradat FAQs

What is Abdelkareem M Jaradat's h-index at Western University?

The h-index of Abdelkareem M Jaradat has been 3 since 2020 and 3 in total.

What are Abdelkareem M Jaradat's top articles?

The articles with the titles of

SCOPE: Smart Cooperative Parking Environment

Appliances Operation Modes Identification Using States Clustering

Density And Dynamic Time Warping Based Spatial Clustering For Appliance Operation Modes

Smart home energy visualizer: a fusion of data analytics and information visualization

Appliance operation modes identification using cycles clustering

Demand response for residential uses: A data analytics approach

are the top articles of Abdelkareem M Jaradat at Western University.

What are Abdelkareem M Jaradat's research interests?

The research interests of Abdelkareem M Jaradat are: Smart Systems, SHEMSs, Machine Learning

What is Abdelkareem M Jaradat's total number of citations?

Abdelkareem M Jaradat has 14 citations in total.

What are the co-authors of Abdelkareem M Jaradat?

The co-authors of Abdelkareem M Jaradat are Anwar Haque.

    Co-Authors

    H-index: 17
    Anwar Haque

    Anwar Haque

    Western University

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