• Smart Media Journal
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• v.12 no.11
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• pp.18-26
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• 2023

The building energy management system (BEMS), a system designed to efficiently manage energy production and consumption, aims to address the variable nature of power consumption within buildings due to their physical characteristics, necessitating stable power supply. In this context, accurate prediction of building energy consumption becomes crucial for ensuring reliable power delivery. Recent research has explored various approaches, including time series analysis, statistical analysis, and artificial intelligence, to predict power consumption. This paper analyzes the strengths and weaknesses of the Prophet model, choosing to utilize its advantages such as growth, seasonality, and holiday patterns, while also addressing its limitations related to data complexity and external variables like climatic data. To overcome these challenges, the paper proposes an algorithm that combines the Prophet model's strengths with the gated recurrent unit (GRU) to forecast short-term (2 days) and medium-term (7 days, 15 days, 30 days) building energy consumption. Experimental results demonstrate the superior performance of the proposed approach compared to conventional GRU and Prophet models.

• Journal of KIISE
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• v.44 no.9
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• pp.954-965
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• 2017

A stable power supply is very important for the maintenance and operation of the power infrastructure. Accurate power consumption prediction is therefore needed. In particular, a university campus is an institution with one of the highest power consumptions and tends to have a wide variation of electrical load depending on time and environment. For this reason, a model that can accurately predict power consumption is required for the effective operation of the power system. The disadvantage of the existing time series prediction technique is that the prediction performance is greatly degraded because the width of the prediction interval increases as the difference between the learning time and the prediction time increases. In this paper, we first classify power data with similar time series patterns considering the date, day of the week, holiday, and semester. Next, each ARIMA model is constructed based on the classified data set and a daily power consumption forecasting method of the university campus is proposed through the time series cross-validation of the predicted time. In order to evaluate the accuracy of the prediction, we confirmed the validity of the proposed method by applying performance indicators.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.4
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• pp.407-413
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• 2011

This paper presents an electrical power monitoring system for home energy management and an automatic appliance-identification algorithm based on the electricity-usage patterns collected during the monitoring tests. This paper also discusses the results of the field tests of which the proposed system was voluntarily deployed at 13 homes. The proposed monitoring system periodically measures the amount of power consumption of each appliance with a pre-specified time interval and effectively displays the essential information provided by the monitored data which is required users to know in order to save power consumption. Regarding the field tests of the monitoring system, the households responded that the system was useful in saving electricity and especially the electricity-usage patterns per appliances. They also considered that the predicted amount of the monthly power consumption was effective. The proposed appliance-identification algorithm uses 4 patterns: Zero-Crossing Rate(ZC), Variation of On State(VO), Slope of On State(SO) and Duty Cycle(DC), which are applied over the 2 hour interval with 25% of it on state, and it yielded 82.1% of success rate in identifying 5 kinds of appliances: refrigerator, TV, electric rice-cooker, kimchi-refrigerator and washing machine.

• Journal of Industrial Convergence
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• v.4 no.2
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• pp.57-73
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• 2006

The purpose of this study is to analyze the recent consumption patterns and consumer's perception changes in the Korean beef market and thus to analyze whether there was any structural changes in beef consumption patterns in Korea. This current survey was conducted to examine consumer attitudes toward factors determining beef purchasing and improving distribution system. First, it is vital to cut beef production costs and expand high-quality beef in order to compete with imported beef. Second, it is also important to endeavor to enhance th safety of beef. Also consumers demand more information on the quality of beef. Third, promotion activities are very important to maintain Hanwoo beef market power. Forth, it is important to prevent imported beef from being sold as Hanwoo beef at retailers. Finally, we should pay more attention to maintain beef consumption data.

• IEIE Transactions on Smart Processing and Computing
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• v.2 no.4
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• pp.248-254
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• 2013

The size and power consumption of digital circuits including the dimming circuit part will increase for high-performance solid state lighting (SSL) systems in the future. This study examined the low-power consumption of adiabatic dynamic CMOS logic (ADCL) due to the principles of adiabatic charging. Furthermore, the designed low-power ADCL digital pulse width modulation (PWM) was optimized for SSL dimming systems. For this purpose, an ADCL digital 3-bit PWM was optimized in two steps. In the first step, the architecture of the ADCL digital 3-bit PWM was miniaturized. In the second step, the clock cut-off circuit was designed and added to the ADCL PWM. As a result, compared to the original configuration, 60 transistors and 15 capacitors of ADCL digital 3-bit PWM were reduced for miniaturization. Moreover, the clock cut-off circuit, which controls wake-up and sleep mode of ADCL D-FFs, was designed. The power consumption of an optimized ADCL digital PWM for all bit patterns decreased by 54 %.

• The Korean Journal of Applied Statistics
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• v.34 no.6
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• pp.969-977
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• 2021

We cluster the electricity consumption of households in A-apartment in Seoul, Korea using Hierarchical K-means clustering algorithm. The data is recorded from the advanced metering infrastructure (AMI), and we focus on the electricity consumption during evening weekdays in summer. Compare to the conventional clustering algorithms, Hierarchical K-means clustering algorithm is recently applied to the electricity usage data, and it can identify usage patterns while reducing dimension. We apply Hierarchical K-means algorithm to the AMI data, and compare the results based on the various clustering validity indexes. The results show that the electricity usage patterns are well-identified, and it is expected to be utilized as a major basis for future applications in various fields.

• International Journal of Internet, Broadcasting and Communication
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• v.9 no.2
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• pp.1-10
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• 2017

The high costs of electric vehicle supply equipment (EVSE) and installation are currently a stumbling block to the proliferation of electric vehicles (EVs). The cost-effective solutions are needed to support the expansion of charging infrastructure. In this paper, we develope EV charging system based on the big data analysis of the power consumption patterns. The developed EV charging system is consisted of the smart EV outlet, gateways, powergates, the big data management system, and mobile applications. The smart EV outlet is designed to low costs of equipment and installation by replacing the existing 220V outlet. We can connect the smart EV outlet to household appliances. Z-wave technology is used in the smart EV outlet to provide the EV power usage to users using Apps. The smart EV outlet provides 220V EV charging and therefore, we can restore vehicle driving range during overnight and work hours.

• Culinary science and hospitality research
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• v.13 no.3
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• pp.293-307
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• 2007

This article was to provide information enabling us to respond effectively to the well being market which has great potential of growth by studying well-being consumption behaviour according to the lifestyles of dining-out customers and to find out how their lifestyles have influence on well-being by investigating their patterns according to demographical characteristics of dining-out customers who play key role in consumption and will have great purchasing power in food service industry. First, factor analysis of variation of lifestyle, 6 factors are named conscious style, realistic style, self-regard style, health-focusing style, changeable style, and fashion-sensitive style. Second, factor analysis of well-being consumption behaviour, 5 factors over eigen 1 are selected and used in a research and they are named healthful food principle, physical health principle, mental health principle, confidence principle, and old-age planning principle. Analysis result reveals that there exists significant relationship between lifestyle and well-being consumption behaviour.

• Journal of the Optical Society of Korea
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• v.19 no.1
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• pp.13-21
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• 2015

In recent years energy consumption has become a main concern for network development, due to the exponential increase of network traffic. Potential energy savings can be obtained from a load-adaptive scheme, in which a day can be divided into multiple time periods according to the variation of daily traffic patterns. The energy consumption of the network can be reduced by selectively turning off network components during the time periods with light traffic. However, the time segmentation of daily traffic patterns affects the energy savings when designing multiperiod logical topology in optical wavelength routed networks. In addition, turning network components on or off may increase the overhead of logical topology reconfiguration (LTR). In this paper, we propose two mixed integer linear programming (MILP) models to design the optimal logical topology for multiple periods in IP-over-WDM networks. First, we formulate the time-segmentation problem as an MILP model to optimally determine the boundaries for each period, with the objective to minimize total network energy consumption. Second, another MILP formulation is proposed to minimize both the overall power consumption (PC) and the reconfiguration overhead (RO). The proposed models are evaluated and compared to conventional schemes, in view of PC and RO, through case studies.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.1
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• pp.72-81
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• 2014

Recently, because of irregular power demand, we have suffered from an electric power shortage. The necessity of the adoption of smart grid which makes effective supply of power by using the two-way communication across the grid between the customers and electric energy providers is growing more and more. If smart grid set up in our country, the third-parties which provide services to customer using the information acquired from smart grid, might be revved up. In this paper, we suggest a methodology how classify the types of family by analysing an power consumption pattern using data mining technique. To make a classifier for categorizing the household types, we need power consumption data and their family type. However, it is hard to get both of them. Therefore we develop the simulator that generates power consumption patterns of the household and classify the types of family. Also, we present a potential for application services such as customized services for a specific family or goods marketing.