• Environmental and Resource Economics Review
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• v.32 no.3
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• pp.149-166
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• 2023

The emissions trading system, introduced to reduce greenhouse gas emissions, experienced a sharp increase in emission allowance prices during the second plan period (2018-2020), which led to an increase in the demand for smooth supply and demand of emission allowances, while suppliers anticipating a shortage of emission allowances in the future did not participate in trading. Therefore, the authority temporarily revised the guidelines to ensure that the amount of allowances carried forward is proportional to the trading volume as a market stabilization measure. Through an optimization process using a dynamic nonlinear mathematical model, this paper analyzes the impact of the government's intervention on the carryover policy on GHG emission reductions and emission allowance market prices. According to the simulation analysis results, banking regulations could cause a decline in prices during the regulation period, even though the initial policy was predicted to be adopted.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.6
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• pp.562-576
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• 2013

Power TAC (Power Trading Agent Competition) is an agent-based simulation for competitions between electricity brokering agents on the smart grid. To win the competition, agents obtain electricity from the electricity wholesale market among the power plants. In this operation, a key to success is balancing the demand of the customer and the supply from the plants because any imbalance results in a significant penalty to the brokering agent. Given the bidding on the wholesale market requires the price and the quantity on the electricity, this paper proposes four different price estimation strategies: exponentially moving average, linear regression, fuzzy logic, and support vector regression. Our evaluations with the competition simulation show which strategy is better than which, and which strategy wins in the free-for-all situations. This result is a crucial component in designing an electricity brokering agent in both Power TAC and the real world.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.57-62
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• 2023

This research is a comparative analysis of the U.S. S&P 500 index using the volatility breakout strategy against the Buy and Hold approach. The volatility breakout strategy is a trading method that exploits price movements after periods of relative market stability or concentration. Specifically, it is observed that large price movements tend to occur more frequently after periods of low volatility. When a stock moves within a narrow price range for a while and then suddenly rises or falls, it is expected to continue moving in that direction. To capitalize on these movements, traders adopt the volatility breakout strategy. The 'k' value is used as a multiplier applied to a measure of recent market volatility. One method of measuring volatility is the Average True Range (ATR), which represents the difference between the highest and lowest prices of recent trading days. The 'k' value plays a crucial role for traders in setting their trade threshold. This study calculated the 'k' value at a general level and compared its returns with the Buy and Hold strategy, finding that algorithmic trading using the volatility breakout strategy achieved slightly higher returns. In the future, we plan to present simulation results for maximizing returns by determining the optimal 'k' value for automated trading of the S&P 500 index using artificial intelligence deep learning techniques.

• KIEAE Journal
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• v.17 no.3
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• pp.75-81
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• 2017

Purpose: The energy consumption of apartment units is affected by the lifestyle of the residents rather than system technology. In this study the numerical analysis of assumed energy consumption correlation factors with arbitrary value due to uncertainty. It is intended to be used as a simulation correction value which can be utilized as a predicted value of actual energy usage. The correction value of the simulation is set in the developed form of the existing process that derives the actual usage amount. The simulation results used in the existing evaluation system are used to maintain the useful value as the current system evaluation scale and predict the actual capacity. Method: The method of the study is to statistically analyze the data frames of all complexes capable of collecting the annual energy usage and to reconstruct the population by adding the variables that are expected to be correlated. Repeat the data frame configuration with variables that are assumed to be highly correlated with energy use levels. Determine whether there is correlation or not. The intensity of the external characteristics of the building equipment related to the energy consumption is presented as the quantitative value. Result: The correlation between electricity consumption and trading price since 2010 is analyzed as (Correlation coefficient 0.82). These results are higher than (Correlation coefficient 0.79), which is the correlation between residential area and trading price. This paper signifies the starting point of the methodology that broadens the field of view of verification of simulation feasibility limited to the prediction technique focused on the simulation tool and the element technology scope.The diversified phenomenon reproduction method develops the existing energy simulation method.It can be completed with a simulation methodology that can infer actual energy consumption.

• Journal of the Korean Data and Information Science Society
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• v.22 no.2
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• pp.255-267
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• 2011

The aim of this study is to utilize system trading for making investment decisions and use technical analysis and Dynamic Time Warping (DTW) to determine similar patterns in the frequency of stock data and ascertain the optimal timing for trade. The study will examine some of the most common patterns in the futures market and use DTW in terms of their frequency (10, 30, 60 minutes, and daily) to discover similar patterns. The recognized similar patterns were verified by executing trade simulation after applying specific strategies to the technical indicators. The most profitable strategies among the set of strategies applied to common patterns were again applied to the similar patterns and the results from DTW pattern recognition were examined. The outcome produced useful information on determining the optimal timing for trade by using DTW pattern recognition through system trading, and by applying distinct strategies depending on data frequency.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.99-99
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• 2011

최근에 미국에서 출범한 수질 교환 법은 수질기준을 혁신적인 접근방법으로 만족시키는 법이다. 이 법안은 수질기준을 초과하지 않는 조건에서 한 유역 안 다른 지점들의 점 오염과 비점오염 배출의 교환을 허용한다. 이 법안을 적용하기 위한 방법을 도출하기 위하여 많은 시험 프로그램을 운영하고 있지만 여전히 실제 교환은 상대적으로 적게 이루어지고 있다. 또한 하천내의 비 점오염량의 불확실성으로 인하여 교환 지점을 선정하고 적용하는 데에 큰 어려움이 있다. Hydrological Simulation Program-Fortran (HSPF)은 Soil and Water Assessement Tool (SWAT)과 함께 미국 하천 모델링에 많이 쓰이는 유역 모델로써 특히 HSPF는 각각의 토지 피복도의 퍼센트 투수량을 지정함으로써 도시지역의 유출량을 시뮬레이션 하는데 강점이 있다. 미국 중서부 미조리 주의 퍼시픽시를 포함하고 있는 Brush Creek 유역을 선택하여 퍼시픽시의 도시화 증가로 인한 Brush Creek 유역의 상류와 하류지역의 유출량 및 Sediment 변화를 예측하여 수질관리법을 적용하는 방법을 연구하였다. 이 연구의 특징은 원격탐사 이미지 (QuickBird)로 구현한 최근의 토지 이용을 미래의 도시지역으로 전환한 토지이용도를 사용함으로서 특정 유역을 가장 정확하게 이해하는 시뮬레이션을 가능하도록 한다는 점이다. 각각의 토지이용에서 도시화가 3가지의 강도를 가지고 진행된다는 시나리오를 이용하여 모델링을 하였고 이로 인해 계산된 유출량과 Sediment 양을 이용하여 각각의 토지이용 변화 별 수질 교환단위를 도출 하였다.

• Management Science and Financial Engineering
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• v.11 no.3
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• pp.1-17
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• 2005

Agent technology has been widely applied in today's electronic business, such as mobile agents, multi-agent information systems, etc. In particular, multi-agent systems have been applied as powerful simulation tools to study complex business networks composed of various self-interested trading firms and/or human beings. In this paper, we build an integrated model that consists of a multi-agent B2C market model and a B2B trade network model, and incorporate more reality than much of prior work. Then with this model, we carry out experimental studies on two different strategies that are common in electronic business - 'loyal' strategy (retailers try to build stable cooperation with suppiers to ensure material supply) and 'cost-saving' strategy (retailers try to reduce cost by choosing suppliers with lower wholesale price).

• Proceedings of the Korea Water Resources Association Conference
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• 2017.05a
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• pp.184-184
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• 2017

A nation-wide water-energy-food (WEF) nexus simulation model has been developed by the authors and successfully applied to South Korea to predict the sustainability of those three resources in the next 30 years. The model was also capable of simulating future scenarios of resources allocation based on priority rules aiming to maximize resources sustainability. However, the process was still relying on several assumptions and trial-and-error approach, which sometimes resulted in non-optimal solutions of resources allocation. In this study, an optimization module was introduced to enhance the model in generating optimal resources management rules. The objective of the optimization was to maximize the reliability index of resources by determining the resources' allocation and/or priority rules for each demand type that accordingly reflect the resources management policies. Implementation of the optimization module would result in balanced allocation and management of limited resources and assist the stakeholders in deciding resources' management plans, either by fulfilling the domestic production or by global trading.

• The Journal of the Korea Contents Association
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• v.12 no.2
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• pp.472-482
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• 2012

There are many investors in the stock market, and more and more people get interested in the stock investment. In order to avoid risks and make profit in the stock investment, we have to determine several aspects using various information. That is, we have to select profitable stocks and determine appropriate buying/selling prices and holding period. This paper proposes a data mining tool for the investors' decision support. The data mining tool makes stock investors apply machine learning techniques and generate stock price prediction model. Also it helps determine buying/selling prices and holding period. It supports individual investor's own decision making using past data. Using the proposed tool, users can manage stock data, generate their own stock price prediction models, and establish trading policy via investment simulation. Users can select technical indicators which they think affect future stock price. Then they can generate stock price prediction models using the indicators and test the models. They also perform investment simulation using proper models to find appropriate trading policy consisting of buying/selling prices and holding period. Using the proposed data mining tool, stock investors can expect more profit with the help of stock price prediction model and trading policy validated on past data, instead of with an emotional decision.

• Environmental and Resource Economics Review
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• v.31 no.2
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• pp.261-290
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• 2022

Although market stabilization measures have been triggered in the K-ETS, carbon price is still under uncertainty. Considering Korea's 2030 enhanced reduction target announced in October 2021, it is crucial to have practical stabilization measures to appropriately deal with price uncertainty. This study examines the quantitative effects of a price collar, which is considered as a means of alleviating price uncertainty, on expected cumulative emissions and abatement costs. There are three main scenarios: carbon tax, emissions trading system, and emissions trading system with a price collar. Monte Carlo simulation was conducted to reflect uncertainty in emission. There are several results as follows: 1) In a price collar, domestic emission target is likely to be achieved with a lower expected abatement cost than other scenarios. In addition, there is a small amount of excess emissions in this research and it would be not critical(0.1% excess than target); 2) Prohibiting banking increases the expected abatement cost. This is because firms can not intertemporally reallocate allowances to match the firm's optimal emissions path; 3) With the adoption of a price collar, government's net revenue can be positive even if the government's purchase volume of emissions allowances is more than sales volume. This is because the government sells them at price ceiling and purchases them at price floor.