A Power Line Communication Modem Based on Adaptively Received Signal Detection for Networked Home Appliances

Kim, Dong–Sun; Lee, Seung-Yerl; Wang, Kyu-Yeul; Choi, Jongchan; Chung, Dae-Won

doi:10.1109/tce.2007.4341558

Cited by 16 publications

(7 citation statements)

References 13 publications

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“…Thus, the communication infrastructure based on a local concentrator is very similar to the one used in advanced metering infrastructures (AMI), where a local concentrator is also used for collecting electricity usage data from residential electricity meters [36], using Wi-Fi or PLC (power line communication technologies) [37].…”

Section: Methodsmentioning

confidence: 99%

Power-system protection device with IoT-based support for integration in smart environments

et al. 2018

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This paper proposes a power-system protection device designed to be integrated in smart environments based on Internet-of-Things technologies. The proposed system enhances electrical safety by fast disconnection of the power supply in case of fault events like leakage current, electrical arc, overcurrent or overvoltage and has been designed with the goal to be integrated in smart environments like smart homes or smart cities for protecting the electrical equipment. The system also enables real-time monitoring and notification events through an advanced communication interface using a data concentrator architecture. This paper provides an extended description of the proposed system’s design and implementation, as well as the experimental validation results.

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Section: Methodsmentioning

confidence: 99%

Power-system protection device with IoT-based support for integration in smart environments

et al. 2018

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“…Also, because the test building has 24 floors with over 400 rooms, the large number of electrical loads and long runs of wires drastically increase PLC channel attenuation (as high as 80 dB) and noise [3]. In similar works with field demonstrations, experiments are typically performed in laboratory environments [28] or two-storey three-bedroom residential houses with less challenging PLC conditions [29].…”

Section: A Experimental Setupmentioning

confidence: 99%

Distributed Load Control Using Reliable Low-Data-Rate Power Line Communication

et al. 2022

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Many demand-response schemes fail because they inconvenience consumers or add unnecessary costs [1], [2]. When participating in a typical demand-response scheme, a consumer may be subjected to invasive control systems, communication systems, or data collection. These demand-response schemes can quickly lose their novelty and may annoy customers by curtailing availability of their appliances, such as electric water heaters or air conditioners [2]. This paper demonstrates a self-organizing demand response scheme that uses power lines as a natural, "free" infrastructure for communication. The approach can shave peak demand substantially with no inconvenience to or attention from the user. Tailored signaling schemes and a custom application of low-data-rate power line communication (PLC) enable loads to reliably selforganize their demand. Field experiments with thermostatically-controlled loads in an actively occupied, 24-floor apartment building demonstrate the advantages of the low-data-rate PLC scheme for energy control.INDEX TERMS demand-side management, low-data-rate communication, media access control, network access protocol, power line communication, time division multiple access I. BACKGROUND

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“…Thus, a power line carrier (PLC) is used to transmit communication information. 13 However, PLC cannot be integrated with other systems. By contrast, wireless communication systems have several advantages, such as ease of expansion.…”

Section: Wired Communication Systems Include Bacnetmentioning

confidence: 99%

Application of neural network to building environmental prediction and control

Zhang

Liu

et al. 2019

Building Services Engineering Research and Technology

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Energy conservation, environmental protection, and intelligence are topics of interest in intelligent buildings. However, the energy requirement of various electrical equipment in intelligent buildings increases energy consumption. This study presents a neural network-based prediction and control system for the regulation of building environmental parameters. Neural network-based soft sensing technology can detect building environmental parameters through few sensors. The proposed system control algorithm can realize the adaptive adjustment of environmental parameters by using a neural network proportional–integral–derivative controller. Zigbee wireless communication is adopted as the information transmission medium to realize the environmental parameter measurement and network control. The soft sensing technique combined with Zigbee communication technology can effectively reduce energy consumption. The central control system analyzes the data coming from the network and regulates the environmental parameter through lifting temperature, ventilation, and switching curtains by using the neural network proportional–integral–derivative algorithm. The regulation of environmental parameters reduces unnecessary energy consumption. Finally, the effectiveness of the system is verified through simulations. Practical applications: This work reports an energy saving scheme. The building communication system constructed by ZigBee can reduce energy consumption and can be easily expanded. The soft sensing technique based on artificial neural network can predict temperatures by using few sensors. The neural network proportional–integral–derivative control algorithm has good performance in the regulation of environmental parameters for a time-varying system. Building energy consumption can be reduced by conducting these measures.

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A Power Line Communication Modem Based on Adaptively Received Signal Detection for Networked Home Appliances

Cited by 16 publications

References 13 publications

Power-system protection device with IoT-based support for integration in smart environments

Power-system protection device with IoT-based support for integration in smart environments

Distributed Load Control Using Reliable Low-Data-Rate Power Line Communication

Application of neural network to building environmental prediction and control

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