• Journal of Electrical Engineering and Technology
• /
• 제12권6호
• /
• pp.2166-2176
• /
• 2017

Electricity price prediction plays a crucial part in making the schedule and managing the risk to the competitive electricity market participants. However, it is a difficult and challenging task owing to the characteristics of the nonlinearity, non-stationarity and uncertainty of the price series. This study proposes a hybrid improved strategy which incorporates data preprocessor components and a forecasting engine component to enhance the forecasting accuracy of the electricity price. In the developed forecasting procedure, the Seasonal Adjustment (SA) method and the Ensemble Empirical Mode Decomposition (EEMD) technique are synthesized as the data preprocessing component; the Coupled Simulated Annealing (CSA) optimization method and the Least Square Support Vector Regression (LSSVR) algorithm construct the prediction engine. The proposed hybrid approach is verified with electricity price data sampled from the power market of New South Wales in Australia. The simulation outcome manifests that the proposed hybrid approach obtains the observable improvement in the forecasting accuracy compared with other approaches, which suggests that the proposed combinational approach occupies preferable predication ability and enough precision.

• 응용통계연구
• /
• 제13권2호
• /
• pp.321-328
• /
• 2000

요일구성, 공휴일 및 윤년에 따른 영업일수 변동은 월별 또는 분기별 경제지표 일시적으로 변동시킴에 따라 지표분석의 교란요인으로 작용하고 있다. 본고에서는 경제지표에서 영업일수 변동의 효과를 RegARIMA모형 등으로 추정한 후 이를 원지표로부터 조정하였다. 그 결과 영업일수를 조정한 지표가 조정하지 않은 지표에 비해 경제지표의 기조적 움직음일 잘 나타내는 것으로 나타났다.

• 농업과학연구
• /
• 제50권4호
• /
• pp.629-643
• /
• 2023

The purpose of this study was to examine the effectiveness and efficiency of a policy by comparing and analyzing the impact of the rice market isolation system and production adjustment system (strategic crops direct payment system that induces the cultivation of other crops instead of rice) on rice supply, rice price, and government's financial expenditure. To achieve this purpose, a rice supply and demand forecasting and policy simulation model was developed in this study using a partial equilibrium model limited to a single item (rice), a dynamic equation model system, and a structural equation system that reflects the casual relationship between variables with economic theory. The rice policy analysis model used a recursive model and not a simultaneous equation model. The policy is distinct from that of previous studies, in which changes in government's policy affected the price of rice during harvest and the lean season before the next harvest, and price changes affected the supply and demand of rice according to the modeling, that is, a more specific policy effect analysis. The analysis showed that the market isolation system increased government's financial expenditure compared to the production adjustment system, suggesting low policy financial efficiency, low policy effectiveness on target, and increased harvest price. In particular, the market isolation system temporarily increased the price during harvest season but decreased the price during the lean season due to an increase in ending stock caused by increased production and government stock. Therefore, a decrease in price during the lean season may decrease annual farm-gate prices, and the reverse seasonal amplitude is expected to intensify.

• 한국수산과학회지
• /
• 제27권6호
• /
• pp.733-753
• /
• 1994

해수면의 장주기변동을 알기 위하여 우리나라, 일본, 러시아의 103개 조위관측점의 월평균 해수면을 분석하였다. 기압보정에는 조위관측점 부근의 기상관측점에서 관측된 월평균 해수면기압을 사용하였다. 계절변화는 대부분의 해역에서 지배적이며, 대마해류역 해안에서 가장 크고 러시아 해안에서 가장 작다. 계절변화의 상호상관관계는 대마해류역 해안 사이에서 가장 크다. 이 부속해에서는 계절변화가 대마해류와 관계를 갖고 있는 것으로 보인다. 계절변화는 남쪽에서 북쪽 뿐만 아니라 서쪽에서 동쪽으로도 전파되고 있다. 반면에, 계절변화보다 장주기의 변화는 태평양 연안에서 가장 큰 진폭과 가장 빠른 위상을 보여, 장주기의 변화는 태평양에서 부터 전파되어 오는 것을 보여준다. 계절변화보다 짧은 주기의 변화는 일반적으로 상관관계가 낮다. 이들의 상관관계 값들은 해역사이에 특별한 차이를 보이지 않으며, 거리에 반비례하는 공통적인 경향을 보이고 있다. 이것은 짧은 주기의 파들이 전해역에 걸쳐 발생하여 모든 방향으로 전파되고 있으며 빨리 소멸한다는 것을 의미한다. 1965부터 1985년 동안 이 해역에서 해수면변화의 경향은 일반적으로 태평양연안에서 음의 기울기를 다른 해역이 양의 기울기를 갖는다. 이러한 경향으로 인해 제주와 Sasebo사이의 평균 해수수송량은 이 기간동안 약 1 Sv의 유량이 줄어들 수 있다. 해수면 차이로부터 계산한 수송량의 계절변화는 대한해협에서 2 Sv 정도로 이미 발표된 다른 연구 보고와 비슷하다.

• 한국가스학회지
• /
• 제8권4호
• /
• pp.15-22
• /
• 2004

본 연구는 LNG 기지 내 주요 프로세스 설비인 LNG 고압펌프의 운전유량 및 토출압력을 조절함으로서 공정운영 조건을 개선하기 위해 수행되었다. 공정 해석 시뮬레이터인 ASPEN PLUS를 사용한 고압펌프의 실제 운전 성능분석 및 천연가스 송출부하 분석을 통하여 계절별 적정 LNG 고압펌프 토출유량을 결정하였고 그 결과는 현장운전에 적용되었다. 이로 인하여 고압펌프 소모 전력비용을 낮출 수 있으며, LNG기지 내 운영 프로세스 압력을 감소 시켜 보다 안전적인 기지운영을 유도할 수 있었다.

• 대기
• /
• 제34권2호
• /
• pp.107-121
• /
• 2024

In this study, we empirically analyzed the impact of physical risks due to climate change on the soundness and operational performance of the financial industry by combining economics and climatology. Particularly, unlike previous studies, we employed the Seasonal-Trend decomposition using LOESS (STL) method to extract trends of climate-related risk variables and economic-financial variables, conducting a two-stage empirical analysis. In the first stage estimation, we found that the delinquency rate and the Bank for International Settlement (BIS) ratio of commercial banks have significant negative effects on the damage caused by natural disasters, frequency of heavy rainfall, average temperature, and number of typhoons. On the other hand, for insurance companies, the damage from natural disasters, frequency of heavy rainfall, frequency of heavy snowfall, and annual average temperature have significant negative effects on return on assets (ROA) and the risk-based capital ratio (RBC). In the second stage estimation, based on the first stage results, we predicted the soundness and operational performance indicators of commercial banks and insurance companies until 2035. According to the forecast results, the delinquency rate of commercial banks is expected to increase steadily until 2035 under assumption that recent years' trend continues until 2035. It indicates that banks' managerial risk can be seriously worsened from climate change. Also the BIS ratio is expected to decrease which also indicates weakening safety buffer against climate risks over time. Additionally, the ROA of insurance companies is expected to decrease, followed by an increase in the RBC, and then a subsequent decrease.

• 전기학회논문지
• /
• 제57권8호
• /
• pp.1342-1348
• /
• 2008

One important objective of the electricity market is to decrease the price by ensuring stability in the market operation. Interconnected to this is another objective; namely, to realize sustainable consumption of electricity by equitably distributing the effects and benefits of participating in the market among all participants of the industry. One method that can help achieve these objectives is the ^{(R)}$demand-response program, - which allows for active adjustment of the loadage from the demand side in response to the price. The demand-response program requires a customer baseline load (CBL), a criterion of calculating the success of decreases in demand. This study was conducted in order to calculate undistorted CBL by analyzing the correlations between such external or seasonal factors as temperature, humidity, and discomfort indices and the amounts of electricity consumed. The method and findings of this study are accordingly explicated.

• 한국태양에너지학회 논문집
• /
• 제37권6호
• /
• pp.69-77
• /
• 2017

The amount of solar irradiation obtained by a photovoltaic (PV) solar panel is the major factor determining the power generated by a PV system, and the array tilt angle is critical for maximizing panel radiation acquisition. There are three types of PV systems based on the manner of setting the array tilt angle: fixed, semi-fixed, and tracking systems. A fixed system cannot respond to seasonal solar altitude angle changes, and therefore cannot absorb the maximum available solar radiation. The tracking system continually adjusts the tilt angle to absorb the maximum available radiation, but requires additional cost for equipment, installation, operation, and maintenance. The semi-fixed system is only adjusted periodically (usually seasonally) to obtain more energy than a fixed system at an overall cost that is less than a tracking system. To maximize semi-fixed system efficiency, determining the optimal tilt angle adjustment schedule are required. In this research, we conducted a simulation to derive an optimal operation schedule for a semi-fixed system in Seoul, Korea (latitude $37.5^{\circ}$). We implemented a solar radiation acquisition model and PV genereation model on MATLAB. The optimal operation schedule was derived by changing the number of tilt angle adjustments throughout a year using a Dynamic Algorithm. The results show that adjusting the tilt angle 4 times a year was the most appropriate. and then, generation amount of PV system increased 2.80% compared with the fixed system. This corresponds to 99% compared to daily adjustment model. This increase would be quite valid as the PV system installation area increased.

• 한국해양학회지
• /
• 제20권1호
• /
• pp.12-21
• /
• 1985

하계 동지나해에서 가장 중요한 해양학적 현상들을 재검토하였다. 계절적 수 온약층 상부 표층수는 태양가열과 주로 양자강으로 부터의 담수의 유입 그리고 하 계 계절풍에 의해 지대한 영향을 받고 있다. 수온약층 하부층에는 여러가지 해양역 학적 작용에 대한 질량장의 조정에 의해서 몇가지 분명히 구별되는 수괴들 즉 쿠로 시오 표층수, 서북태평양 중앙수, 황해저층 냉수등이 잠입되고 있다. 잠입된 황해저 층 냉수와 서북태평양 중앙수와의 전선역 혼합이 제주 남방 대륙붕상의 저층에서 일어난다. 이 혼합수는 제주 주변과 한국 남해안 중저층의 해수 특성에 커다란 영 향을 미칠 것으로 보인다.

• 한국수자원학회:학술대회논문집
• /
• 한국수자원학회 2021년도 학술발표회
• /
• pp.373-373
• /
• 2021

Rainfall forecasting is an important issue that is applied in many areas, such as agriculture, flood warning, and water resources management. In this context, this study proposed a statistical and machine learning-based forecasting model for monthly rainfall. The Bayesian Gaussian process was chosen to optimize the hyperparameters of the Stacked Long Short-term memory (SLSTM) model. The proposed SLSTM model was applied for predicting monthly precipitation of Seoul station, South Korea. Data were retrieved from the Korea Meteorological Administration (KMA) in the period between 1960 and 2019. Four schemes were examined in this study: (i) prediction with only rainfall; (ii) with deseasonalized rainfall; (iii) with rainfall and minimum temperature; (iv) with deseasonalized rainfall and minimum temperature. The error of predicted rainfall based on the root mean squared error (RMSE), 16-17 mm, is relatively small compared with the average monthly rainfall at Seoul station is 117mm. The results showed scheme (iv) gives the best prediction result. Therefore, this approach is more straightforward than the hydrological and hydraulic models, which request much more input data. The result indicated that a deep learning network could be applied successfully in the hydrology field. Overall, the proposed method is promising, given a good solution for rainfall prediction.