• Journal of the Korean housing association
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• v.22 no.6
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• pp.43-50
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• 2011

Korea is a country with an exceptionally high energy consumption. For economic reasons, Korean households are forced to save more energy. Korea's household energy consumption has grown slowly compared to other sectors and household energy consumption per capita is lower than the OECD average. However, its per capita electricity consumption soared and is expected to remain climbing mainly due to the increasing number of one-person households. To establish an effective strategy against a possible electricity shortage, the actual condition survey of electricity energy consumption first needs to be clearly understood. This study adopted both a general survey and a detailed survey of people living in apartment housings and data was collected on electrical appliance use according to individual schedules. Based on these data, the results were used to attempt to analyze electricity consumption patterns resulting from energy using activities of residents and to determine electricity consumption propensity according to each household member's characteristics in apartment housings.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.5 no.7
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• pp.1274-1292
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• 2011

Due to the variety of household electric devices and different power consumption habits of consumers at present, general home energy management (HEM) systems suffer from the lack of dynamic identification of various household appliances and a unidirectional information display. This study presented a set of intelligent interconnection network systems for electric appliances, which can measure the power consumption of household appliances through a current sensing device based on OSGi platform. The system establishes the characteristics and categories of related electric appliances, and searches the corresponding cluster data and eliminates noise for recognition functionality and error detection mechanism of electric appliances by applying the clustering algorithm. The system also integrates household appliance control network services so as to control them according to users' power consumption plans or through mobile devices, thus realizing a bidirectional monitoring service. When the system detects an abnormal operating state, it can automatically shut off electric appliances to avoid accidents. In practical tests, the system reached a recognition rate of 95%, and could successfully control general household appliances through the ZigBee network.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.35 no.7
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• pp.743-748
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• 2011

The performance of HFO-1234yf as a substitute for R-134a was evaluated in a household freezer/refrigerator. A series of tests such as 'refrigerant charging test, pull-down test, cooling speed test, and energy consumption test were carried out under the AHAM (Association of Home Appliance Manufacturers) standard. The results of a drop-in test were compared with those of a test conducted using R-134a. A test under a severe ambient air condition ($43^{\circ}C$) was also conducted. The result shows that the refrigeration cycle performance of HFO-1234yf is as good as that of R-134a ; however, the diameter of capillary tube should be increased in order to improve its performance in the cooling speed test.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1821-1830
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• 2018

With the wide application of intelligent household appliances, the optimization of electricity behavior has become an important component of home-based intelligent electricity. In this study, a multi-objective optimization model in an intelligent electricity environment is proposed based on economy and comfort. Firstly, the domestic consumer's load characteristics are analyzed, and the operating constraints of interruptible and transferable electrical appliances are defined. Then, constraints such as household electrical load, electricity habits, the correlation minimization electricity expenditure model of household appliances, and the comfort model of electricity use are integrated into multi-objective optimization. Finally, a continuous search multi-objective particle swarm algorithm is proposed to solve the optimization problem. The analysis of the corresponding example shows that the multi-objective optimization model can effectively reduce electricity costs and improve electricity use comfort.

• Proceedings of the KIEE Conference
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• 2006.11a
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• pp.216-218
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• 2006

Economy of world has grown rapidly. This growth has increased the ownership of household electrical appliances including room air conditioners. The number of users of air conditioners is predicted to grow dramatically in the future. This paper discusses the present status of residential appliance energy efficiency standards around the world. The study found that the energy efficiency standards for room all conditioners would mitigate a significant amount of emissions in Korea.

• Journal of Families and Better Life
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• v.15 no.4
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• pp.81-102
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• 1997

This study intended to investigate factors shaping the styles for comsumers to express their dissatisfaction after comsuming goods(cloth and household appliance) and services(public and health) In particular this study examined what kinds of characteristics were crucial to distinguish three styles of consumers' and public complaints. The characteristics of consumer to be considered in this study included consumer knowledge consumer attitude consumer and several socio-economic characteristics. The sample used in this study were consumers whose age was grater than 20 years old living near Seoul in 1996. Discriminant analysis was conducted to investigate what factors discriminate the style of complaint. This study found that several consumer characteristics were sigificant in explaining different styles for consumers to response their dissatisfactions. The effects of consumer characteristics were more significant in explaining the complaining styles derived from public and health services rather than goods. Overall consumer attitude consumer knowledge and the degree of satisfaction of services were discriminant variables in explaining the styles of consumer complaint. Both consumer knowledge and budgeting skill were significant in explaining complaint styles to response dissatisfaction derived from consuming clothing while both consumer education and time constraint were significant in shaping the kinds of complaining styles derived from consuming household appliances.

• Archives of design research
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• v.17 no.4
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• pp.5-14
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• 2004

Black and white TV design process at early electronics industry can help greatly in strategy of forward product development or production and marketing. Because, there is history about existed all product to precess that progress. Past information make good use that estimate of new design. According to character of product, advertisement and business strategies are available some extension of life cycle or preservation. But, it is need the data accumulated with correct analysis for ability that competitive power of the product. Data of Past information take advantage of well information is essential. Information about past products arranged pamphlet and newspaper advertisement. At this study emphasized on console model TV and portable style TV design change process of technology ability from 1960s to 1980s about product selling point. Creation and extinction about products can get into iteration to other household electric appliance. Design process of past product design has a history with our international location or influence power of household electric appliance. Therefore, such past flowing can important role as basis of forward product design.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.21 no.9
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• pp.1718-1724
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• 2017

Recently, Smart plugs for real time monitoring of household appliances based on IoT(Internet of Things) have been activated. Through this, consumers are able to save energy by monitoring real-time energy consumption at all times, and reduce power consumption through alarm function based on consumer setting. In this paper, we measure the alternating current from a wall power outlet for real-time monitoring. At this time, the current pattern for each household appliance was classified and it was experimented with deep learning to determine which product works. As a result, we used a cross validation method and a bootstrap verification method in order to the classification performance according to the type of appliances. Also, it is confirmed that the cost function and the learning success rate are the same as the train data and test data.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.20 no.6
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• pp.381-387
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• 2008

The presence of non-condensable gases as an additional thermal resistance inside a refrigerating circuit has been found for a general refrigerator, The effect of non-condensable gases was varied by controlling the injection amount of dry air into the refrigerating circuit to increase a thermal resistance. Energy consumption tests for the refrigerator were conducted under the various amounts of non-condensable gases. The tested refrigerating circuit was the household refrigerator. As the molar fraction of non-condensable gases was increased from 0% to 1.46%, the amount of energy consumption was found to increase up to 25%. The increase of the amount of non-condensable gases in refrigerating circuit was found to result in increasing the condensation temperature at the condenser and decreasing the evaporation temperature at the evaporator, which were presumably caused by the low specific heat and increased partial pressure of non-condensable gas.

• Journal of the Korea Convergence Society
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• v.12 no.2
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• pp.21-28
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• 2021

In this study, we propose a deep learning-based NILM technique using actual measured power data for 5 kinds of home appliances and verify its effectiveness. For about 3 weeks, the active power of the central power measuring device and five kinds of home appliances (refrigerator, induction, TV, washing machine, air cleaner) was individually measured. The preprocessing method of the measured data was introduced, and characteristics of each household appliance were analyzed through spectogram analysis. The characteristics of each household appliance are organized into a learning data set. All the power data measured by the central power measuring device and 5 kinds of home appliances were time-series mapping, and training was performed using a LSTM neural network, which is excellent for time series data prediction. An algorithm that can disaggregate five types of energies using only the power data of the main central power measuring device is proposed.