• Smart Media Journal
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• v.8 no.4
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• pp.72-79
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• 2019

The automated demand response (DR) program encourages consumers to participate in grid operation by reducing power consumption or deferring electricity usage at peak time automatically. However, successful deployment of the automated DR program sphere needs careful assessment of appliances load profile (ALP). To this end, the recent method estimates frequency, consistency, and peak time consumption parameters of the daily ALP to compute their potential score to be involved in the DR event. Nonetheless, as the daily ALP is subject to varying with respect to the DR time ALP, the existing method could lead to an inappropriate estimation; in such a case, inappropriate appliances would be selected at the automated DR operation that effected a consumer comfort level. To address this challenge, we propose a more proper method, in which all the three parameters are calculated using ALP that overlaps with DR time, not the total daily profile. Furthermore, evaluation of our method using two public residential electricity consumption data sets, i.e., REDD and REFIT, shows that our energy management systems (EMS) could properly match a DR target. A more optimal selection of appliances for the DR event achieves a power consumption decreasing target with minimum comfort level reduction. We believe that our approach could prevent the loss of both utility and consumers. It helps the successful automated DR deployment by maintaining the consumers' willingness to participate in the program.

• Journal of Industrial Technology
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• v.30 no.A
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• pp.9-15
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• 2010

Recently, Smart Grid is a emerging topic in power and communication industry. Smart Grid refers to a evolution of the electricity supply infrastructure that monitors, protects, and intelligently optimize the operation of the interconnected elements including various type of generators, power grid, building/home automation system and end-use consumers. In order to successful implementation of Smart Grid, energy management function will be the key factor that coordinates and optimally controls the various loads according to the operating condition and environments, and the load patterns in residential and commercial building will be required as fundamental element for load management. In this study, we collects many types of energy usage data of electric appliances, analyze their load curves, and make the general load patterns for electrical appliance.

• Proceedings of the IEEK Conference
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• 2003.11c
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• pp.51-54
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• 2003

This paper describes the design and implementation of HNCP bundle in the OSGi framework targeted to the home network system. The developed home network system is composed of a home server and several networked home appliances, and is connected via residential gateway to the external access network. The developed HNCP bundle allows a user to control and monitor home appliances in HNCP domain. We show the feasibility of the implemented HNCP bundle in the OSGi framework.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.10
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• pp.269-283
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• 2021

The government policies and initiatives to guarantee sustainable energy and clean environmental conditions contributed to the introduction of green technology electricity appliances in the market. This study sought to determine the physiological and socio-economics-demographic factors driving residential electricity consumers to use green technology electricity appliances, mainly solar PV, smart meter, electric vehicle, and battery storage technology. By understanding consumer intention, the investors of solar PV, battery storage, electric vehicle, and smart meter can estimate the demand and upscale the market for the corresponding products. For that purpose, the intention to use the solar PV, smart meter, electric vehicle, and battery storage function is developed by utilizing the combination of the theory of planned behavior, technology acceptance, and reasoning action. A reliable and valid structured online questionnaire and stepwise multiple regression are used to identify the possible factors that drive consumer behavior intention. The results show that the social influence, knowledge on RE, and perceived price significantly influence residential consumers' willingness to adopt the technologies offered. The findings of this study suggest that the involvement of NGOs, public figures, and citizens' cooperation are all necessary to spread information about the government's objectives and support Malaysia's present energy and environmental policies.

• International Journal of Advanced Culture Technology
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• v.6 no.4
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• pp.284-291
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• 2018

As environmental pollution has become worse green technologies to replace or reduce consumption of fossil fuel get spotlight from government, industry and academia globally. It is reported that 40% of carbon dioxide emission is caused by electricity power generation. And 37% of end user electricity power is used by residential costumer in US. Smart Grid is considered as one of promising technology to alleviate severe environmental problem. In residential environment, Home Energy Management System (HEMS) can play a key role for green smart home. The HEMS can give several benefits like aslowering electric utility bill, improvement of efficiency of electric power consumption and integration of generator using renewable energy resources. However just limited functions of HEMS can be used for residential customer in real life because of lack of smart function in home appliances and optimal managing software for HEMS. This study provides comprehensive analysis for Home Energy Management System for residential customer. Simple HEMS system with real products on the market are explained and limitation of current HEMS are also discussed.

• Journal of Electrical Engineering and Technology
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• v.8 no.3
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• pp.572-580
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• 2013

Power metering and monitoring system is a basic element of Smart Grid technology. This paper proposes a new Non-Intrusive Load Monitoring (NILM) method for a residential buildings sector using the measured total active power consumption. Home electrical appliances are classified by ON/OFF state models, Multi-state models, and Composite models according to their operational characteristics observed by experiments. In order to disaggregate the operation and the power consumption of each model, an algorithm which includes a switching function, a truth table matrix, and a matching process is presented. Typical profiles of each appliances and disaggregation results are shown and classified. To improve the accuracy, a Time Lagging (TL) algorithm and a Permanent-On model (PO) algorithm are additionally proposed. The method is validated as comparing the simulation results to the experimental ones with high accuracy.

• Proceedings of the KIPE Conference
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• 2001.10a
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• pp.687-691
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• 2001

With today's global environmental and energy problems, high expectations exist for solar power generation to reduce carbon dioxide generated by the consumption of fossil fuels. On the other hand, power consumption in residential homes is increasing every year. Among the many household appliances, the power demand for air conditioners increases dramatically during the summer, particularly in the afternoons. As this pattern closely matches the output pattern of solar cells, it should be possible to combine a photovoltaic array with an air conditioner to decrease the energy consumption within the home. We have developed a residential solar-powered air conditioner that operates through a combination of photovoltaic array and commercial power. In this paper, the configuration and specification of the residential solar-powered system are described to a novel high efficiency inverter using a ZVCS boost converter. And the performance evaluations of the solar-powered air conditioner are examined by the analysis of a new tracking controller with a maximum power $P_{max}$ detection of solar cell.

• Korean Institute of Interior Design Journal
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• v.21 no.2
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• pp.231-242
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• 2012

The purposes of this study were to identify the needs for ubiquitous home services in residential environment of elderly single or elderly couple households living without children and also to analyze the differences of the needs according to their demographic and housing characteristics. For this study, a literature review and field works on ubiquitous home services for older people were performed. Also, 1 to 1 interview by using the questionnaire which was developed by the researchers in this study was conducted and 270 elderly residents in Seoul and Gyeonggi-Do were responded. Seventy-one ubiquitous service items which were adopted to home electronic appliances and furniture for older people were developed for 1 to 1 interview questionnaire. The major findings of the study were as follows: the elderly residents expressed highest need for ubiquitous home service items related to safety and health issues. Among ubiquitous service items applied to gas stove, vacuum cleaner, TV, telephone, sofa and toilet, the items related to safety and health aspects were most needed. And residents' income level and health status were the two major variables to show group differences in the need of ubiquitous home services. In other words, the elderly residents who were high income and frail tended to have the highest demand for ubiquitous home services adopted to home electronic appliances and furniture.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.183-191
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• 2021

Non-intrusive load monitoring is a technology that can be used for predicting and classifying the type of appliances through real-time monitoring of user power consumption, and it has recently got interested as a means of energy-saving. In this paper, we propose a system for classifying appliances from user consumption data by combining GAF(Gramian angular field) technique that can be used for converting one-dimensional data to the two-dimensional matrix with convolutional neural networks. We use REDD(residential energy disaggregation dataset) that is the public appliances power data and confirm the classification accuracy of the GASF(Gramian angular summation field) and GADF(Gramian angular difference field). Simulation results show that both models showed 94% accuracy on appliances with binary-state(on/off) and that GASF showed 93.5% accuracy that is 3% higher than GADF on appliances with multi-state. In later studies, we plan to increase the dataset and optimize the model to improve accuracy and speed.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.68-77
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• 2018

Regarding demand response (DR) by residential users (R-users), the users try to reduce electricity costs by adjusting their power consumption in response to the time-varying price. However, their power consumption may be affected not only by the price, but also by user convenience for using appliances. This paper proposes a methodology for appliance scheduling (AS) that considers the user convenience based on historical data. The usage pattern for appliances is first modeled applying the copula function or clustering method to evaluate user convenience. As the modeling results, the comfort distribution or representative scenarios are obtained, and then used to formulate a discomfort index (DI) to assess the degree of the user convenience. An AS optimization problem is formulated in terms of cost and DI. In the case study, various AS tasks are performed depending on the weights for cost and DI. The results show that user convenience has significant impacts on AS. The proposed methodology can contribute to induce more DR participation from R-users by reflecting properly user convenience to AS problem.