• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.87-106
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• 2024

Vehicle-to-Home (V2H) and Home Centralized Photovoltaic (HCPV) systems can address various energy storage issues and enhance demand response programs. Renewable energy, such as solar energy and wind turbines, address the energy gap. However, no energy management system is currently available to regulate the uncertainty of renewable energy sources, electric vehicles, and appliance consumption within a smart microgrid. Therefore, this study investigated the impact of solar photovoltaic (PV) panels, electric vehicles, and Micro-Grid (MG) storage on maximum solar radiation hours. Several Deep Learning (DL) algorithms were applied to account for the uncertainty. Moreover, a Reinforcement Learning HCPV (RL-HCPV) algorithm was created for efficient real-time energy scheduling decisions. The proposed algorithm managed the energy demand between PV solar energy generation and vehicle energy storage. RL-HCPV was modeled according to several constraints to meet household electricity demands in sunny and cloudy weather. Simulations demonstrated how the proposed RL-HCPV system could efficiently handle the demand response and how V2H can help to smooth the appliance load profile and reduce power consumption costs with sustainable power generation. The results demonstrated the advantages of utilizing RL and V2H as potential storage technology for smart buildings.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.17 no.11
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• pp.1056-1063
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• 2007

Recently, there has been a growing consumer interest in the amount of noise produced by household electrical appliances. The designer of the product must identify the source of the noise, in order to reduce the noise. In the case of a household electric appliance such as the washing machine, there is consumer's complaint about the noise that is generated during the dehydrating condition. Because of several noise sources combined each other. It is difficult to identify the noise sources that contribute to the noise output. Multi-Dimensional Spectral Analysis (MDSA) is a method that can remove the correlation between different noise sources, and it expresses the key contributing factor as a unique output. This study utilized MDSA to analyze the contribution of each input in the noise output during the dehydrating condition.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.5
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• pp.730-737
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• 2016

Electric utilities may face new threats with increase in electric vehicles (EVs) in the personal automobile market. The peak demand will increase which may stress the distribution network equipment. The focus of this paper is on an adaptive control of smart household appliances by using an intelligent load management system (ILMS). The main objectives are to accomplish consumer needs and prevent overloading of power grid. The stress from the network is released by limiting the peak demand of a house when it exceeds a certain point. In the proposed strategy, for each smart appliance, the customers will set its order/rank according to their own preferences and then system will control the household loads intelligently for consumer reliability. The load order can be changed at any time by the customer. The difference between the set and actual value for each load's specific parameter will help the utility to estimate the acceptance of this intelligent load management system by the customers.

• Journal of the Korea Safety Management & Science
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• v.23 no.4
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• pp.23-29
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• 2021

The purpose of this study is to reduce the probability of occurrence of electric fires as a preemptive preventive measure, and to strengthen the capability of preventing electric fires by strengthening the cooperative function between electric fire-related departments and establishing a cooperative system. In this study, the general aspects of electric fires were identified by reviewing the literature such as ignition mechanisms of electric fires. And the major electrical fires that occurred in the last 10 years were classified into ignition factors (short circuit, overload/overcurrent, and earth leakage/ground fault) and ignition sources (wiring/wiring appliances, electrical equipment/household appliances). And the 4M technique was used to analyze the potential causes of ignition at the fire site and to suggest preventive measures. In the case In this study, out of 48 electrical fires in the past 10 years, 16 short-circuit fires, 3 overload/overcurrent fires, 3 short-circuit and earth fault fires, 16 fires in wiring/wiring appliances, and 10 fires in electrical equipment/home appliances classified as cases. And prevention measures were presented in terms of human, machine, media, and management by using the 4M technique. For the preemptive prevention of electric fires, strengthening the compulsory electrical safety inspection and making it mandatory to report when new or expanding electric facilities, charging a fee for electric safety inspection for detached houses and granting benefits subject to inspection completion, improvement of the electric safety voluntary inspection table and safety indications; It was suggested as a policy to organize and operate electrical safety inspection personnel in a two-person team (mixed), establish a close work cooperation system with related organizations, and strengthen electrical safety education and publicity.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.4
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• pp.34-42
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• 2017

Since existing nonintrusive appliance load monitoring (NIALM) studies assume that voltage fluctuations are negligible for load identification, and do not affect the identification results, the power factor or harmonic signals associated with voltage are generally not considered parameters for load identification, which limits the application of NIALM in the Smart Home sector. Experiments in this paper indicate that the parameters related to voltage and the characteristics of harmonics should be used to improve the accuracy and reliability of the load monitoring system. Therefore, in this paper, we propose an improved NIALM method that can efficiently analyze the types of household appliances and electrical energy usage in a home network environment. The proposed method is able to analyze the energy usage pattern by analyzing operation characteristics inherent to household appliances using harmonic characteristics of some household appliances as recognition parameters. Through the proposed method, we expect to be able to provide services to the smart grid electric power demand management market and increase the energy efficiency of home appliances actually operating in a home network.

• Journal of KIISE:Computing Practices and Letters
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• v.7 no.5
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• pp.402-415
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• 2001

Advantages of flash memories are their shock resistance and fast read speed, which is much faster than that of a HDD. Because of these characteristics, they are increasingly used in the traditional household electric appliance and portable handset and therefore, development of file systems which use them as storage medium is increasingly needed. But they have two problems as storage medium. First, data stored in them cannot be overwritten: it must be erased before new data can be stored. Unfortunately, this erase operation usually takes about one second. Consequently, updating data in flash memories takes long time. In this paper, their problem is solved by using a data update mechanism like LFS(Log-structured File System). Second, their erase operations are restricted. We propose novel cleaning policy in order to increase the life cycle. We implemented FAT file system, which is suitable to small storage medium and solved problems, which usually happen in implementing FAT. We evaluated the performance of sequential writes and random writes on our implemented flash file system.