• Journal of Information Technology Services
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• v.11 no.sup
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• pp.41-50
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• 2012

With the recent advances in smart grid technologies and the increasing dissemination of smart meters, the power usage of each time unit can be detected in modern smart building environments. Thus, the utility company can adopt different price of electricity at each time slot considering the peak time. Korea government also announces the smart-grid roadmap that includes a law for realtime price of electricity. In this paper, we propose an efficient power scheduling scheme for smart buildings that adopt smart meters and real-time pricing of electricity. Our scheme dynamically changes the power mode of each consumer device according to the change of power rates. Specifically, we analyze the electricity demands and prices at each time, and then perform real-time power scheduling of consumer devices based on collaboration of each device. Experimental results show that the proposed scheme reduces the electricity charge of a smart building by up to 36.4%.

• Journal of the Korean Geographical Society
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• v.48 no.2
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• pp.272-287
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• 2013

This paper describes the importance of selecting explanatory variables(e.g. land price determinants) in hedonic pricing models employed in predicting real estate price, and explores dynamics of the land price determinants over time. The City of Junju was chosen as the study area, and repeated measured price data of standard lots over 17 years were analyzed. We applied a three-level modeling approach to this data in consideration of its nested data structure and longitudinal characteristics. Main land price determinants we focused on are primarily based on items included in the standard comparison table of land price, which is an official hedonic pricing model used by Government to estimate land price for tax levy. Our result shows that the land price fluctuation over 17 years was not uniform over the whole study area with each neighborhood revealing different price trend, and as such warrants longitudinal model components. In addition, some of determinants previously recognized as important were proved insignificant. It was also found that significant determinants at a particular time point lost its power gradually over time and vice versa. It is expected that more accurate prediction of price would be possible when taken account for this dynamics of price determinants over time in applying hedonic pricing model method.

• Journal of Digital Convergence
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• v.11 no.11
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• pp.37-52
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• 2013

Prior to full-scale implementation of smart grid, the Korean government is conducting a smart grid testbed in Jeju island. However, the participants of the ongoing program are skeptical about the success of the expansion of smart grid. The concern rises from various reasons; the limits of the Korean electricity market mainly led by both the government and KEPCO, high stability and reliability of the existing electricity grid, insufficient utilization of renewable energy, and public fear of raised electricity bills. Five key issues in regards to facilitating the Korean smart grid market are extracted and evaluated. The issues are conflict of interest among participants, the effect of introducing real-time pricing, lack of customer participation of demand response, and absence of business models.

• Journal of Digital Convergence
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• v.14 no.7
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• pp.193-200
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• 2016

Research and development of energy self-consumption introducing photovoltaic and energy storage system at home is very active. This system can manage the home energy in which it charges the electricity generated during the day and uses it during high electricity bills. However, it not yet made up the residential real-time pricing in Korea but it can reduce electricity usage to a certain target on the progressive. In order to introduce the home photovoltaic, it requires PCS(Power Conditioning System). This converts the direct current into alternating current by the electricity generated and used to perform charging and discharging of the energy storage system. The market for self-consumption smart home system is currently increasing because the interests of the general public about solar power, energy storage systems increased. The result of this study is installed on the room environment and the effect was analyzed on the assumption of real-time pricing.

• Journal of the Korea Convergence Society
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• v.12 no.10
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• pp.9-17
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• 2021

With the advancement of big data analysis, artificial intelligence, machine learning, etc., data analytics technology has developed to help with optimal decision-making. However, in certain areas, the lack of data restricts the use of these techniques. For example, real estate related data often have a long release cycle because of its recent release or being a non-liquid asset. In order to overcome these limitations, we studied the scalability of the existing time series through the TimeGAN model. A total of 45 time series related to weekly real estate data were collected within the period of 2012 to 2021, and a total of 15 final time series were selected by considering the correlation between the time series. As a result of data expansion through the TimeGAN model for the 15 time series, it was found that the statistical distribution between the real data and the extended data was similar through the PCA and t-SNE visualization algorithms.

• The Journal of Bigdata
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• v.8 no.1
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• pp.23-34
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• 2023

Nowadays, the Designated Driver Services employ dynamic pricing, which adapts in real-time based on nearby driver availability, service user volume, and current weather conditions during the user's request. The uncertain volatility is the main cause of price increases, leading to customer attrition and service refusal from driver. To make a good Designated Driver Services, development of a demand forecasting model is required. In this study, we propose developing a demand forecasting model using data from the Designated Driver Service by considering normal and peak periods, such as rush hour and rush day, as prior knowledge to enhance the model performance. We propose a new methodology called Time-Series with Conditional Probability(TSCP), which combines conditional probability and time-series models to enhance performance. Extensive experiments have been conducted with real Designated Driver Service data, and the result demonstrated that our method outperforms the existing time-series models such as SARIMA, Prophet. Therefore, our study can be considered for decision-making to facilitate proactive response in Designated Driver Services.

• Environmental and Resource Economics Review
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• v.16 no.1
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• pp.99-127
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• 2007

When the margin between available capacity and demand is thin in a liberalized electricity market, prices rise steeply and system reliability is threatened. The principal response to these circumstances is often an assumption that price spikes and electricity shortages are the result of a failure to build sufficient new supplying facilities. It is, of course, often the case that additional investments in generation and network facilities would improve reliability, and such investments are often needed. But focusing on additional generation and transmission facilities for restoring balance to the grid overlooks the essential fact that reliability is a function of the relationship between supply and demand, imposing unnecessary costs on electric system. When the relationship is out of balance, the search for solutions must consider not only investments supply-side resources but also cost-effective demand-side resources such as accelerated load management, efficiency measures, and price-responsive load programs. Integrating demand resources into electricity markets can add enormous value to the electric system, widening the capacity margin, lowering costs and enhancing system reliability at the same time. This paper studies several challenges now facing electricity markets: demand-side management-especially, economic effects of demand response, potential reliability problems, market and system operation, CBP market improvements and so on. The paper concludes with a series of policy recommendations in five areas: (i) The Effects of efficient improvement to incorporate demand responses and demand-side resources into modem electricity markets, (ii) Fosteing price based demand response and (iii) improving incentive based demand response, (iv) strengthen demand response analysis and valuation, (v) integrating demand response into resource planning and adopting enabling technologies.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.6
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• pp.2826-2832
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• 2012

A smart grid is an electric-power grid that employs a new information technology.This technology makes it possible to exchange real-time energy information between suppliers and consumers, finally resulting in high energy efficiency. The energy management system in smart grid provides up to date information on electricity consumption as well as dynamic electricity price to consumers of smart grid system. However, the existing energy management systems only focus on pricing system, for example, real-time electricity prices. In this paper, we try to improve the existing energy management system and propose the energy management system that mainly focuses on the efficiency of electricity consumption. In the proposed management system, PMU(Phasor Measurement Units) installed in switchboards gathers electricity data in a real time. We also propose to use data mining method, which is applied to analyzed electricity data for improving energy efficiency. Also, the proposed energy management system is designed to efficiently control the electricity between PMU and management system in case of a shortage of electricity or surplus electricity.

• Economic and Environmental Geology
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• v.41 no.5
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• pp.551-562
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• 2008

Option pricing model in finance has been applied to price non-financial options, called real options. The real option valuation method is ideally suited to irreversible decision making under uncertainty, including the need to determine the optimal time to act and even change between alternative courses of action as information is collected. Therefore, the real option valuation method is expected to provide a superior and less subjective approach to determining optimal strategies for water resources supply projects, which have been reported to have huge risks due to uncertainties, and investors and policy makers need to build an optimal strategy - when and if to invest - with uncertainties and managerial flexibilities considered.

• Journal of the Korea Computer Industry Society
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• v.6 no.2
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• pp.241-248
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• 2005

Conventional estimating methods forecast the future that it usually using the past statistical numerical value. In order to forecast the farming price, it must need many effort and accuracy knowledge. Therefore, to solve the these problems, this paper to improve forecasting farming price using fuzzy rules and neural network as a preprocessing. Also, we developed an intelligent farming expert system for real time forecasting as a postprocessing about unexpectable conditions. Computer simulation results proved reducing pricing error which proposed farming price expecting system better than conventional demand forecasting system does not using fuzzy rules.