Hybrid user action prediction system for automated home using association rules and ontology

Shaban, Amneh; Almasalha, Fadi; Qutqut, Mahmoud H.

doi:10.1049/iet-wss.2018.5032

Cited by 5 publications

(2 citation statements)

References 26 publications

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“…The user can manage the devices remotely using electronic devices such as smartphones, personal devices, and laptops. Recent technological developments have also generated many intelligent and preceding systems that can promote intelligent technology in life (Shaban et al 2019). One of the benefits of having a smart home is the ability to monitor energy consumption, which is unattainable in non-smart homes.…”

Section: Introductionmentioning

confidence: 99%

Centralized smart energy monitoring system for legacy home appliances

Ahmad,

Almasalha,

Qutqut

et al. 2024

Energy Inform

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The increasing global population and reliance on electrical devices for daily life resulted in sharply rising energy consumption. Also, this leads to higher household electricity bills. As a result, there is a growing demand for energy monitoring systems that can accurately estimate energy usage to help save power, especially for older home appliances that are difficult or expensive to update with monitoring sensors. However, current energy monitoring systems have some drawbacks, such as the inability to detect different types of appliances and the deployment complexity. Moreover, such systems are too costly to use in older power infrastructures. To address this issue, we proposed a centralized smart energy monitoring system designed for legacy home appliances, aiming to address the limitations of current energy monitoring systems by avoiding costly infrastructure upgrades to calculate the power consumption of legacy home appliances. The proposed system employs a two-layered architecture comprising hardware (Emontx device, Analog-to-Digital Converters (ADC), and Current Transformer (CT) sensors) and a software layer that includes Artificial Intelligence (AI) predictors using a pre-defined set of rules and K Nearest Neighbours (KNN) algorithms. We conducted three experiments on real home appliances to evaluate the proposed work. The accuracy of the proposed system showed positive results after several modifications and hard tuning of several parameters in devices, specifically for Jordanian power plants.

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Section: Introductionmentioning

confidence: 99%

Centralized smart energy monitoring system for legacy home appliances

Ahmad,

Almasalha,

Qutqut

et al. 2024

Energy Inform

Self Cite

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“…ese di erent systems also have di erences in equipment connection mode, data transmission format, and so on. erefore, in view of the heterogeneity and multisource of data perception of the Internet of things, how to interconnect and integrate di erent data has attracted extensive attention of relevant scholars [2].…”

Section: Introductionmentioning

confidence: 99%

A Semantic Model of Internet of Things for Intelligent Translation and Learning

Wei

2022

Mathematical Problems in Engineering

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There were a lot of multisource data and heterogeneous devices in the intelligent system of the Internet of things, and the existing methods were difficult to meet the service needs of users for intelligent entities. Therefore, this paper proposed a semantic model construction method of the Internet of things based on intelligent translation and learning. Firstly, on the basis of summarizing the relevant theories of semantic Internet of things, this paper analyzed the semantic data and its characteristics, and expounded the common ontology matching methods. Secondly, according to the characteristics of service ontology and user ontology in intelligent Internet of things system, a method of matching two different ontologies based on string and semantic relationship was proposed, and the cyclic neural network method was used to organically integrate the semantic data of ontology. Finally, in order to realize the perception and representation of the context information of the Internet of things, a semantic model of the Internet of things based on intelligent translation and learning was constructed. Through experimental comparative analysis, the results showed that compared with the traditional methods based on semantic similarity and semantic distance, the semantic model of the Internet of things proposed in this paper had better performance in accuracy and recall, and can achieve better application effect of the Internet of things system. The model proposed in this paper will provide a theoretical reference for further exploring the sharing and service of heterogeneous devices and data in the intelligent Internet of things system.

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