• 한국인공지능학회지
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• 제12권1호
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• pp.17-24
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• 2024

This paper introduces a novel approach to time-series estimation for energy load forecasting within Virtual Power Plant (VPP) systems, leveraging advanced artificial intelligence (AI) algorithms, namely Long Short-Term Memory (LSTM) and Seasonal Autoregressive Integrated Moving Average (SARIMA). Virtual power plants, which integrate diverse microgrids managed by Energy Management Systems (EMS), require precise forecasting techniques to balance energy supply and demand efficiently. The paper introduces a hybrid-method forecasting model combining a parametric-based statistical technique and an AI algorithm. The LSTM algorithm is particularly employed to discern pattern correlations over fixed intervals, crucial for predicting accurate future energy loads. SARIMA is applied to generate time-series forecasts, accounting for non-stationary and seasonal variations. The forecasting model incorporates a broad spectrum of distributed energy resources, including renewable energy sources and conventional power plants. Data spanning a decade, sourced from the Korea Power Exchange (KPX) Electrical Power Statistical Information System (EPSIS), were utilized to validate the model. The proposed hybrid LSTM-SARIMA model with parameter sets (1, 1, 1, 12) and (2, 1, 1, 12) demonstrated a high fidelity to the actual observed data. Thus, it is concluded that the optimized system notably surpasses traditional forecasting methods, indicating that this model offers a viable solution for EMS to enhance short-term load forecasting.

• 물과 미래
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• 제11권2호
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• pp.54-61
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• 1978

비교적 주기성이 강하고 경향성이 존재하는 유량시계열에 있어서 예측 및 모의발생을 위한 모형개발이 시도되었다. 원시계열로부터 구한 차분시계열(Diffe renced time series)이 정상공분산을 갖는다는 가정하여 모형의 고정화(Model Intentification)가 실시되었으며, 정상가정을 정당화하기 위해 잔차(Residual)의 통계적 성질을 검토하였다. 또한, 동정된 모형의 예측 정도를 노이기 위하여 예측오차의 분산이 최소가 되도록 추계적 제어(Stochastic Control)된 모형을 예측에 사용하였다. 한국주요하천유역의 유량자료에 대한 모형의 고정과 예측결과로부터, 차분연산자(Difference operator)는 경향과 주기를 제거하는데 좋은 방법이 됨이 판단되었다.

• Journal of Electrical Engineering and Technology
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• 제12권1호
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• pp.151-160
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• 2017

This paper presents a new technique of reference current generator based on Kalman filter (KF) estimator for three-phase shunt active power filter (APF). The stationary reference frame (d-q algorithm) is used to transform the load currents into DC component. The harmonics of load currents are extracted and the three-phase reference currents are generated using KF estimator. The work is simulated using Matlab/Simulink platform. To validate the simulation results, an experimental test-rig have been perform using real-time control dSPACE DS1104. In addition, hysteresis current control was used to generate the switching signal for the correction of the harmonics in the system. The non-linear load were constructed with three-phase rectifier which connected in series with inductor and parallel with resistor and capacitor. The results shows that the new technique of shunt APF embedded with KF is proven to eliminate the harmonics created by the non-linear load with some improvement on the total harmonics distortion (THD).

• 한국수자원학회논문집
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• 제47권9호
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• pp.825-838
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• 2014

정상성 마코프 연쇄 모형은 일강우모의 모형으로 광범위하게 이용되고 있다. 하지만 정상성 마코프 연쇄 모형의 기본가정은 통계학적 특성이 시간에 따라 변화하지 않는 것으로, 일강우모의 시에 평균 또는 분산의 경향적 변화를 효과적으로 반영할 수 없다. 이러한 문제점을 인지하여 본 연구에서는 연주기 및 계절변화에 대하여 우수한 모의 능력을 나타내는 GCM의 모의결과를 입력자료로 이용하여 일강우량을 모의하기 위한 통계학적 상세화(downscaling) 기법인 비정상성 은닉 마코프 모형을 개발하였다. 개발된 모형을 낙동강 유역에 존재하는 영주지점, 문경지점 및 구미지점의 관측강우량에 적용한 결과, 일단위 및 계절단위의 강우량의 통계적 특성을 기존 모형에 비하여 개선된 결과를 도출할 수 있었으며, 또한 개발된 모형은 극치강수량 복원에 있어서도 관측값과 보다 유사한 결과를 보여 주었다. 이러한 점에서 정확성이 확보된 GCM 계절예측자료가 입력자료로 NHMM 모형에 활용된다면 예측기반의 일강수 상세화 모형으로 활용될 수 있을 것으로 판단된다. 이와 더불어, 기후변화 시나리오 입력자료가 사용된다면 기후변화 상세화 모형으로서도 적용될 수 있을 것으로 사료된다.

• Structural Engineering and Mechanics
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• 제56권6호
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• pp.959-982
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• 2015

In this study, a non-stationary random earthquake Clough-Penzien model is used to describe earthquake ground motion. Using stochastic direct integration in combination with an equivalent linear method, a solution is established to describe the non-stationary response of lead-rubber bearing (LRB) system to a stochastic earthquake. Two parameters are used to develop an optimization method for bearing design: the post-yielding stiffness and the normalized yield strength of the isolation bearing. Using the minimization of the maximum energy response level of the upper structure subjected to an earthquake as an objective function, and with the constraints that the bearing failure probability is no more than 5% and the second shape factor of the bearing is less than 5, a calculation method for the two optimal design parameters is presented. In this optimization process, the radial basis function (RBF) response surface was applied, instead of the implicit objective function and constraints, and a sequential quadratic programming (SQP) algorithm was used to solve the optimization problems. By considering the uncertainties of the structural parameters and seismic ground motion input parameters for the optimization of the bearing design, convex set models (such as the interval model and ellipsoidal model) are used to describe the uncertainty parameters. Subsequently, the optimal bearing design parameters were expanded at their median values into first-order Taylor series expansions, and then, the Lagrange multipliers method was used to determine the upper and lower boundaries of the parameters. Moreover, using a calculation example, the impacts of site soil parameters, such as input peak ground acceleration, bearing diameter and rubber shore hardness on the optimization parameters, are investigated.

• 수산경영론집
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• 제46권2호
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• pp.31-41
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• 2015

This study aimed to analyze the relationship between sea surface temperature as a climatic element and catch amount of offshore and coastal fisheries in Korea using annual time series data from 1970 to 2013. It also tried to predict the future changes in catch amount of fisheries by climate change. Time series data on variables were estimated to be non-stationary from unit root tests, but one long-term equilibrium relation between variables was found from a cointegration test. The result of Granger causality test indicated that the sea surface temperature would cause directly changes in catch amount of offshore and coastal fisheries. The result of regression analysis on sea surface temperature and catch amount showed that the sea surface temperature would have negative impacts on the catch amount of offshore and coastal fisheries. Therefore, if the sea surface temperature would increase, all other things including the current level of fishing effort being equal, the catch amount of offshore and coastal fisheries was predicted to decrease.

• 수산해양교육연구
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• 제27권6호
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• pp.1745-1754
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• 2015

This study aimed to analyze the relationship between sea temperatures and anchovy catch of Anchovy drag net fishery using annual time series data from 1970 to 2013. In the analysis, time series data on variables (CPUE, sea surface temperature, and 10m temperature) were estimated to be non-stationary from unit root tests, but one long-term equilibrium relation among variables was found from a cointegration test. From an exclusion test, a 10m temperature would not have relations with CPUE and sea surface temperature. The result of regression analysis on sea surface temperature and anchovy catch indicated that the sea surface temperature would have positive impacts on the anchovy catch. It means that when the sea surface temperature would increase, all other things including the current level of fishing effort being equal, the catch of anchovy was predicted to increase. More specifically, the result showed that when 1% of sea surface temperature increases, CPUE would be increased by 2.81%.

• 한국항만경제학회지
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• 제34권3호
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• pp.183-200
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• 2018

본 연구는 1996년 10월부터 2017년 4월까지의 건화물선, 컨테이너선, 유조선 가격과 운임 자료를 사용하여 선가의 합리적 거품 유무를 검정하였다. 기존의 연구와 달리, 컨테이너선, 유조선 가격으로 실증분석 범위를 확장하여 모형설정 오류에서 자유로운 안정성에 기초한 안정성 검정과 공적분 검정을 활용하였다. 안정성 검정 결과, 유조선 가격에 거품이 존재하였으며, 공적분 검정은 건화물선과 컨테이너선의 가격에 거품이 포함되었다는 결과를 나타내었다. 이러한 실증분석 결과는 우리나라 해운기업이 저선가 시기에 선박을 확보하는 선박투자 전략을 채택해야하며, 이를 촉진하기 위한 정부의 금융 지원과 안정적인 선복량 확보 정책 수립의 필요성을 시사한다.

• 디지털산업정보학회논문지
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• 제18권3호
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• pp.75-82
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• 2022

The purpose of the Consumer sentiment index survey is to determine the consumer's economic situation and consumption spending plan, and it is used as basic data for diagnosing economic phenomena and forecasting the future economic direction. The purpose of this paper is to analyze and predict the future Consumer sentiment index using the ARMA model based on the past consumer index. Consumer sentiment index is determined according to consumer trends, so it can reflect consumer realities. The consumer sentiment index is greatly influenced by economic indicators such as the base interest rate and consumer price index, as well as various external economic factors. If the consumer sentiment index, which fluctuates greatly due to consumer economic conditions, can be predicted, it will be useful information for households, businesses, and policy authorities. This study predicted the Consumer sentiment index for the next 3 years (36 months in total) by using time series analysis using the ARMA model. As a result of the analysis, it shows a characteristic of repeating an increase or a decrease every month according to the consumer trend. This study provides empirical results of prediction of Consumer sentiment index through statistical techniques, and has a contribution to raising the need for policy authorities to prepare flexible operating policies in line with economic trends.

• 스마트미디어저널
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• 제9권4호
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• pp.26-35
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• 2020

미래의 주가를 예측하기 위한 시도는 과거부터 꾸준히 연구되어왔다. 그러나 일반적인 시계열 데이터와 달리 금융 시계열 비정상성(non-stationarity)과 장기 의존성(long-term dependency), 비선형성(non-linearity) 등 예측을 하는 것에 있어서 여러 가지 방해 요인이 존재한다. 또한, 광범위한 데이터의 변수는 기존에 사람이 직접 선택하는 것에 한계가 있으며 모델이 변수를 자동으로 잘 추출할 수 있도록 하여야 한다. 본 논문에서는 비정상성 데이터를 정규화할 수 있는 슬라이딩 타임스텝 정규화(sliding time step normalization) 방법과 LSTM 형태의 오토인코더(AutoEncoder)를 사용하여 모든 변수로부터 압축된 변수로 미래 주가를 예측하는 방법, 기간을 나누어 전이 학습을 하는 이동 전이 학습(moving transfer learning)을 제안한다. 또한, 실험을 통하여 100개의 주요 금융 변수들만을 사용하는 것보다 뉴럴 네트워크를 통해서 가능한 많은 변수를 사용하였을 때 성능이 우수함을 보이며, 슬라이딩 타임스텝 정규화 방법을 사용하여 모든 구간에서 데이터의 비정상성에 대해 정규화를 수행함으로써 성능 향상에 효과적임을 보인다. 이동 전이 학습 방법은 스텝 별 테스트 구간에서 모델의 성능을 평가하고 전이학습을 함으로써 긴 테스트 구간에서 성능 향상에 효과적임을 보인다.