An IoT-Based Home Energy Management System over Dynamic Home Area Networks

Kim, Jong-Bae; Byun, Jinsung; Jeong, Daebeom; Choi, Myeong-in; Kang, Byeongkwan; Park, Sehyun

doi:10.1155/2015/828023

Cited by 26 publications

(12 citation statements)

References 38 publications

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“…the way to execute the IoT systems, the integration of the WSN with the IoT is a new generation of the M2M (Machine to Machine) Communication and the Scientific research played a valuable role in the Collecting data from sensors and use them in IoT applications [10][11][12][13]. In [14], the authors suggested a system for monitoring which is based on IoT technology, this system uses a triple-level context for offering the awareness services designed contextualization, throughout employing an opensource software and hardware. That test was carried out by employing two scenarios as a smart household based on an IoT-based Monitoring System (IMS).…”

Section: Literature Reviewmentioning

confidence: 99%

Monitoring the Consumption of Electrical Energy Based on the Internet of Things Applications

Hussien

Al-Magsoosi

Alrikabi

et al. 2021

Int. J. Interact. Mob. Technol.

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<p class="0abstract">One of the instrumental functional overheads in many commercial edifices and industrial plants is the electricity bill. The design and development of a Smart Power monitoring device used to make monitoring on the electrical parameters represented as voltage, current, and power of Wasit university buildings. The system has a unit with smart sensing to detect and control the electrical devices employed for daily activities by following different tariff rates. It has the capability to decrease consumer costs improving grid stability. A developed model was broadly examined and experimental results comparing these results with devices of conventional measuring. The Arduino based wireless powering meter is a non-invasive existing meter for domiciliary power with a WI-FI Connection. A split-core is used to measure the current transformers. 802.11b connection is a means for transmitting data through the home’s wireless router to the base station and monitoring web portal based on wireless sensor network. A clear projection for domestic data use is one of the aims of this project. By these data, it can supply estimation to consuming power. Optimizing and reducing power usage are the objectives of providing such data.</p>

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Section: Literature Reviewmentioning

confidence: 99%

Monitoring the Consumption of Electrical Energy Based on the Internet of Things Applications

Hussien

Al-Magsoosi

Alrikabi

et al. 2021

Int. J. Interact. Mob. Technol.

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“…), Then, the specified behaviors (such as load management, demand response, etc.) can be operated by using the Open ADR communication protocol between the terminal units and DN [3,29].…”

Section: Terminal Unitsmentioning

confidence: 99%

Cloud-Fog Architecture Based Energy Management and Decision-Making for Next-Generation Distribution Network with Prosumers and Internet of Things Devices

Yue

et al. 2019

Applied Sciences

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The increasing penetration of distributed energy resources in next-generation distribution networks has resulted in an explosion of the Internet of Things to upgrade their control and monitoring systems. This poses new challenges for the efficient energy management and reliable decision-making of these systems. This is due to the potentially large amount of data that cannot be handled by the traditional architecture of control and data acquisition systems, which have limited storage and computation capabilities. In order to adapt to the new energy management requirements of next-generation distribution networks, a state-of-the-art energy management method called cloud-fog hierarchical architecture is proposed in this work. Based on this architecture, we established a utility and revenue model for various stakeholders, including normal customers, prosumers, and distribution system operators. Furthermore, by embedding an artificial intelligence module in the proposed architecture, energy management could be implemented automatically. Neural networks were used at fog computing layers to achieve regression prediction of energy usage behavior and power source output. Moreover, based on the maximizing utility objective function, the amount of energy consumption of customers and prosumers in the distribution network was optimized with a genetic algorithm at cloud layer. The proposed methods were tested with a set of normal customers and prosumers in a general distribution network, and the results, including the captured usage patterns of the customers and revenues of various stakeholders, verify the effectiveness of the proposed method. This work provides an effective reference for the development of real-time energy management systems for the next-generation distribution network.

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“…In [14], Internet of Things (IoT)-based home vitality the executives framework proposed with lightweight photovoltaic (PV) framework over powerful home territory systems, which bolsters the development of home scalable, reusable, and interoperable energy management method. They proposed methods with lightweight PV framework over powerful home territory systems, which bolsters the development of.…”

Section: Related Workmentioning

confidence: 99%

Reduce Cost Smart Power Management System by Utilize Single Board Computer Artificial Neural Networks for Smart Systems

Ashaj¹,

Erçelebi²

2019

IJCIS

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The plan and usage of a smart power management system for household and buildings that control numerous electrical appliances in real time have been reported in this work. The system is based on using artificial intelligence with low-cost single board computer in order to design a smart power management system that can analyzed some aspects that can serve power management aspects such as, electricity consumption to reduce power consumption to lower limits as possible, temperature to control, and human activity to control lighting and power on/off some devises like TV. A Raspberry PI 3 version B has been utilized as a computer unit, in a fast and accurate way to control, for example, switching lighting/TV when human in or left the area. The system utilized some devises in that purpose that includes, a Raspberry Pi camera to streamed real-time video for detection the existence of human and his activity, an ultrasonic sensor to compute distance of human in area, temperature sensor to detect room temperature in home or buildings in order to control air conditioning systems and odor/gas sensors to control ventilation systems, power sensor to compute electricity consumption. The proposed system is programmed by a used Python programming language that manages all aspect at the same time. The recognition part is based on utilized conversational neural network (CNN) that optimized by used saliency object detection so as to improve the CNN in acknowledgment exactness and acknowledgment speed. The outcomes endorsed that the proposed system can manage the power in smooth and accurate that can serve both electrical consumption and lifestyle where all operation run in fast and automated way, furthermore, the recognition algorithm success in detect objects and isolate it from background with 100% accuracy and in fast time reach to 0.7 seconds.

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An IoT-Based Home Energy Management System over Dynamic Home Area Networks

Cited by 26 publications

References 38 publications

Monitoring the Consumption of Electrical Energy Based on the Internet of Things Applications

Monitoring the Consumption of Electrical Energy Based on the Internet of Things Applications

Cloud-Fog Architecture Based Energy Management and Decision-Making for Next-Generation Distribution Network with Prosumers and Internet of Things Devices

Reduce Cost Smart Power Management System by Utilize Single Board Computer Artificial Neural Networks for Smart Systems

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