Machine Learning for Smart Energy Monitoring of Home Appliances Using IoT

Rashid, Rozeha A.; Chin, Leon; Sarijari, Mohd Adib; Sudirman, Rubita; Ide, Teruji

doi:10.1109/icufn.2019.8806026

Cited by 39 publications

(10 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, the integration of household appliances and consumer electronics is not straightforward at any layer of the sensor network platform. Additionally, apart from the technical difficulties of the integration, due to the complexity of the systems, users are often unable to fully understand their systems [8]. To fully take advantage of smart energy monitoring technologies, any energy monitoring system should transparently communicate with all the household energy meters and make available all important information to the users with respect to saving energy and user convenience [9].…”

Section: Imentioning

confidence: 99%

Performance Analysis of Smart Energy Monitoring Systems in Real-time

Govindarajan¹,

Meikandasivam²,

Vijayakumar³

2020

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

The use of smart energy monitoring systems in buildings offers significant potential for energy saving. The introduction of a new kind of energy monitoring system is essential in the consumer-side in order to meet the demands of energy conservation. In this paper, four energy monitoring systems based on advanced wireless technology are proposed. The design and implementation of the smart real-time energy monitoring systems is analyzed with respect to their performance. The proposed systems utilize advanced wireless technologies like the Zigbee module, Internet of Things (IoT), Android Mobile Apps, and cloud computing for interfacing the information between the meters and the consumer end. A digital power meter is installed in the main panel, interfaced with a communication gateway and parameters such as voltage, current, power, power factor, and the harmonics of household appliances are measured in real-time. These systems provide power consumption details to the consumer and allow interacting with them by giving instant data such as live power tracking, identification of abnormal patterns of energy usage, energy bill estimation, and energy usage information.

show abstract

Section: Imentioning

confidence: 99%

Performance Analysis of Smart Energy Monitoring Systems in Real-time

Govindarajan¹,

Meikandasivam²,

Vijayakumar³

2020

Eng. Technol. Appl. Sci. Res.

View full text Add to dashboard Cite

show abstract

“…The authors in [80] proposed a system that can automatically collect data from smart home devices, process it using machine learning algorithms in the IoT environment, and generated insights about energy consumption patterns. The system consists of a smart plug based on Raspberry Pi, which serves as the gateway to collect data from home appliances.…”

Section: Energy Management In Smart Homes Using Iotmentioning

confidence: 99%

Applications of IoT and digital twin in electrical power systems: A comprehensive survey

Mansour

Numair

Zalhaf

et al. 2023

IET Generation Trans & Dist

View full text Add to dashboard Cite

This paper reviews the applications of Internet of Things (IoT) and digital twin technology in electrical power systems. It begins by discussing the generalized IoT value chain, followed by the terminology of smart grid, with clarifying the role of IoT‐systems and the digital twin structure within the Smart Grid. A comparison between different short‐range and long‐range transports is presented. The paper then discusses the use of IoT and digital twin technology for effective energy management with applications in smart homes, smart buildings, smart grids, smart industries (Industry 4.0), smart transportation and smart cities. Additionally, the paper explores the use of IoT and digital twin technology for condition monitoring and diagnosis (CMD) in electrical power systems. Three different cases are presented for CMD, that is, CMD of power transformers, CMD of electrical grids and CMD of substations. IoT and digital twin applications are also highlighted in power electronic systems. Finally, the paper discusses the challenges and opportunities of applying IoT and digital twin technology to electrical power systems and provides recommendations for future research.

show abstract

“…A monitoring architecture was developed in [20] with the purpose of energy saving in the residential environment, using IoT and Machine Learning techniques. The physical part of the monitoring system makes use of a non-invasive current sensor connected to a Raspberry Pi 3 A+, which transmits information that is then stored in the Pi.…”

Section: Related Literaturementioning

confidence: 99%

A Smart Home Architecture for Smart Energy Consumption in a Residence With Multiple Users

et al. 2021

View full text Add to dashboard Cite

Smart Energy Control Systems (SECS) has been increasingly implemented in the Smart homes scenario, due to the possibility of conditioning and controlling residential energy consumption, thus contributing to reducing energy losses and unnecessary electrical consumption. With the evolution of embedded systems in conjunction with the Internet of Things (IoT), Smart Outlet (SO), and devices that promote the Users Indoor Identification (UII) environments, they have assumed fundamental roles in the acquisition of data from electrical devices and also in the mapping of the individualized consumption of each resident throughout the day, providing essential information for SECS Systems as a way to assist in energy balance with minimal impact on the daily usability of electrical equipment. However, in most of the works that propose these types of assistance to SECS based on SO and UII, they have a massive implementation of sensors throughout the residence, misinterpretation of the data generated by the residents, difficulty in the identification of multiple residents. Thus, the present work proposes an evolution of an SECS architecture called SmartCom, with the implementation of accurate identification of electrical equipment through Near Field Communication (NFC)-based SO (data transfer between the appliance and the SO) and of multiple inhabitants through Wi-Fi handover using smartphones, with the least possible impact on the user's comfort, as well as in the building structure, achieving a rebalanced residential energy consumption 87.3% of the time it was used.

show abstract

Machine Learning for Smart Energy Monitoring of Home Appliances Using IoT

Cited by 39 publications

References 6 publications

Performance Analysis of Smart Energy Monitoring Systems in Real-time

Performance Analysis of Smart Energy Monitoring Systems in Real-time

Applications of IoT and digital twin in electrical power systems: A comprehensive survey

A Smart Home Architecture for Smart Energy Consumption in a Residence With Multiple Users

Contact Info

Product

Resources

About