• Proceedings of the KIEE Conference
• /
• 2011.07a
• /
• pp.656-657
• /
• 2011

산업사회의 급속한 발전과 생활수준 향상에 따라 전력수요 및 공급전망에 대한 인식이 점차 강조되고 있다. 에너지자원이 부족한 우리나라는 전체 에너지의 약 97%를 수입에 의존하고 있으므로 전력공급의 정확한 수요예측을 통해서 안정적, 경제적으로 전력을 공급해야 한다. 2001년 전력산업구조개편에 따라 전력시장은 발전부문만 시장에 참여하여 경쟁하는 발전경쟁체제로 발전사업자의 입찰량과 전력거래소의 전력수요 예측 결과를 이용하여 시간대별 전력시장가격을 결정하는 가격결정발전 계획을 수립하고 있다. 본 논문에서는 청정 녹색에너지로 피크시간대에 발전하여 주파수 조절을 담당함으로써 전력계통에 크게 기여하고 있는 수력 발전기의 최적 입찰 전략 및 수력발전 사업계획에 활용할 수 있는 전력거래가격 전망 전략을 제시하여 수력발전사업자의 수익 증대와 전력시장 가격 안정화에 기여하고자 한다.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2000.04a
• /
• pp.457-460
• /
• 2000

주가를 예측하는 것은 이미 오래 전부터 여러 가지 방법으로 시도되어 왔었다. 기업의 본질가치를 보는 기본적 분석부터 과거의 자료를 가지고 미래를 예측하는 기술적 분석까지 많은 연구가 있었으나 실제로 모든 예측이 그렇듯이 많이 적중을 했다는 것을 일부의 정형화된 분석방법을 제외하고는 찾지 못하였다. 그럼에도 불구하고 이번 연구에서는 기술적 분석에서 많은 요인들 중에서 기존에 많이 연구해 보지 못한 시계열적인 인자를 가지고 단기간의 주가를 예측하고자 한다. 주식이 상한가에 도달하였을 경우 그 상한가격의 잔량과 그 주식의 일일거래량을 비교하여 그 서로 두 관계가 다음날 주가에 어느 정도의 영향을 미치는지 회귀분석을 통하여 상관성을 분석하고 통계적 자료를 토대로 단기간의 주가를 상한 잔량 대비 일일거래량에 비추어 의사결정 지표를 제시하려고 한다. 적절한 예측결과가 나오게 되면 주식에 대해 매수를 희망하는 사람 뿐 아니라 주식을 보유하고 있는 사람에게 어느 정도 정보효과가 미치게 될 것이라 기대한다.

• Proceedings of the KIEE Conference
• /
• 2000.07a
• /
• pp.369-372
• /
• 2000

An overview on applications of wavelet transform in power systems presented in this paper. Wavelet transform is capable of making trade-offs between time and frequency resolutions, which is a property that makes it appropriate for the analysis of non stationary signal. In recent years, wavelet transform is widely accepted as a technology offering an alternative way due to its flexibility in representation of non-stationary signal even in power systems. This paper presents various applications of wavelet transform in power systems. Wavelet transform has been used by the authors in the field of power system protection for the classification of transient signals, and forecasting of short term loads and system marginal price and so on. Various research works carried out by many researchers in power systems are summarized.

• Proceedings of the Korea Society for Simulation Conference
• /
• 2001.10a
• /
• pp.313-317
• /
• 2001

The approach to predict time series without neglecting the fluctuation in a short period is tried by using a digital FIR filter and a neural network. The differential waveform of the Nikkei average closing price is filtered by the FIR band-pass filter of 101 length. It is filtered into the five frequency bands of 0-1Hz, 1-2Hz, 2-3Hz, 3-4Hz and 4-5Hz by setting the sampling frequency 10Hz. The each filtered waveform is learned and forecasted by the neural network. The neural network of the back propagation method is adopted in the learning the waveform. By inputting the data of 20 days in the past, the prediction of 10 days ahead is carried out. After learning the time series of each frequency band by the neural network, the predicted data far each frequency band are obtained. The predicted waveforms of each frequency band are synthesized to obtain a final forecast. The waveform can be forecasted well as a whole.

• Proceedings of the KIEE Conference
• /
• 2008.07a
• /
• pp.580-583
• /
• 2008

As the construction of renewable energy generators is on the rise and gets bigger in size, researchers pay more and more attention to the impact of such facilities on the power market as well as on the stability of power grid system. In Korea, while studies on the latter, including calculating the marginal capacity of renewable energy generators, is being made, those on the former has not yet been performed. As such, this paper analyses the impact of a big renewable energy generators on the price and transaction cost of domestic power market and proposes ideas to minimize such influence by applying the technology of forecasting renewable energy.

• IE interfaces
• /
• v.21 no.3
• /
• pp.343-353
• /
• 2008

Vendor-managed inventory(VMI) is a supply chain strategy to improve the inventory turnover and customer service in supply chain management. Unfortunately, many VMI programs fail because they simply transfer the transactional aspects of placing replenishment orders from customer to vendor. In fact, such VMI programs often degrade supply chain performance because vendors lack capability to plan the VMI operations effectively in an integrated way under the dynamic, complex, and stochastic VMI supply chain environment. This paper presents a decision support system, termed DSSV, for VMI in the retail supply chain. DSSV supports the market forecasting, vendor's production planning, retailer's inventory replenishment planning, vehicle routing, determination of the system operating parameter values, retailer's purchase price decision, and what-if analysis. The potential benefits of DSSV include the provision of guidance, solution, and simulation environment for enterprises to reduce risks for their VMI supply chain operations.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.3 no.1
• /
• pp.7-12
• /
• 2003

In recent years, many attempts have been made to predict the behavior of bonds, currencies, stock or other economic markets. Most previous experiments used the neural network models for the stock market forecasting. The KOSPI 200 (Korea Composite Stock Price Index 200) is modeled by using different neural networks and fuzzy logic. In this paper, the neural network, the dynamic polynomial neural network (DPNN) and the fuzzy logic employed for the prediction of the KOSPI 200. The prediction results are compared by the root mean squared error (RMSE) and scatter plot, respectively. The results show that the performance of the fuzzy system is little bit worse than that of the DPNN but better than that of the neural network. We can develop the desired fuzzy system by optimization methods.

• International journal of advanced smart convergence
• /
• v.5 no.4
• /
• pp.32-39
• /
• 2016

Machine learning is a field of artificial intelligence (AI), and a technology that collects, forecasts, and analyzes securities data is developed upon machine learning. The difference between using machine learning and not using machine learning is that machine learning-seems similar to big data-studies and collects data by itself which big data cannot do. Machine learning can be utilized, for example, to recognize a certain pattern of an object and find a criminal or a vehicle used in a crime. To achieve similar intelligent tasks, data must be more effectively collected than before. In this paper, we propose a method of effectively collecting data.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2003.05a
• /
• pp.329-337
• /
• 2003

Stock market prediction is regarded as a challenging task of financial time-series prediction. There have been many studies using artificial neural networks in this area. Recently, support vector machines (SVMs) are regarded as promising methods for the prediction of financial time-series because they me a risk function consisting the empirical ewer and a regularized term which is derived from the structural risk minimization principle. In this study, I apply SVM to predicting the Korea Composite Stock Price Index (KOSPI). In addition, this study examines the feasibility of applying SVM in financial forecasting by comparing it with back-propagation neural networks and case-based reasoning. The experimental results show that SVM provides a promising alternative to stock market prediction.

• Proceedings of the Korean Information Science Society Conference
• /
• 2006.10a
• /
• pp.500-504
• /
• 2006

본 논문에서 기술하는 연구는 한국종합주가지수(KOSPI)의 장기적 변동 경향에 대한 확률적 예측 시스템을 제안한다. 제안된 방법론은 이미 단백질 상호작용 예측 시스템과 스트레스 확률 예측 시스템 등에 적용되어 유효성이 입증된 방법으로, 이미 알려진 데이터를 바탕으로 다양한 요인들의 가능한 모든 조합에 대한 경우의 수를 고려한 학습 결과에 기반하여 새로이 주어진 대상의 요인들을 분석해서 학습시 사용된 특정 군(class)에 속할지의 여부를 확률적으로 나타내준다. 이 방법론을 구현하기 위해 실제 과거 주가지수 데이터를 수집하여 CI(Combination Interrelation)행렬을 구현하였으며, 현재 진행중인 검증작업에 대해서도 기술하였다.