• Atmosphere
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• v.34 no.2
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• pp.177-185
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• 2024

As increasing global interest in renewable energy due to the ongoing climate crisis, there is a growing need for efficient technologies to manage such resources. This study focuses on the predictive skill of daily solar power generation using weather observation and forecast data. Meteorological data from the Korea Meteorological Administration and solar power generation data from the Korea Power Exchange were utilized for the period from January 2017 to May 2023, considering both inland (Daejeon) and coastal (Busan) regions. Temperature, wind speed, relative humidity, and precipitation were selected as relevant meteorological variables for solar power prediction. All data was preprocessed by removing their systematic components to use only their residuals and the residual of solar data were further processed with weighted adjustments for homoscedasticity. Four models, MLR (Multiple Linear Regression), RF (Random Forest), DNN (Deep Neural Network), and RNN (Recurrent Neural Network), were employed for solar power prediction and their performances were evaluated based on predicted values utilizing observed meteorological data (used as a reference), 1-day-ahead forecast data (referred to as fore1), and 2-day-ahead forecast data (fore2). DNN-based prediction model exhibits superior performance in both regions, with RNN performing the least effectively. However, MLR and RF demonstrate competitive performance comparable to DNN. The disparities in the performance of the four different models are less pronounced than anticipated, underscoring the pivotal role of fitting models using residuals. This emphasizes that the utilized preprocessing approach, specifically leveraging residuals, is poised to play a crucial role in the future of solar power generation forecasting.

• Korean System Dynamics Review
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• v.15 no.3
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• pp.37-59
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• 2014

This paper is to study dynamics of populations of religions. As human population is a crucial source of social dynamics, the religious population is a driving force that changes political and cultural landscape of society. Although many christian scholars have reported important causal factors in changing population of christian world, there are few studies on the dynamics of religious population in system dynamics. This paper interprets these dynamic mechanisms in terms of feedback loops and constructs a basic system dynamic model to forecast future trend of religious population in Korean society.

• Journal of the Korean Professional Engineers Association
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• v.37 no.4
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• pp.55-59
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• 2004

Exploitation of the ocean's deep water has brought humanity a wealth of treasures for centuries. Even so, it can confidently be forecast that the Ocean will be far more important to future generations than it has ever been in the past. Although many researchers endeavor to explore oceans, the ocean holds the crucial elements for maintaining a growing industrialized population in search of raw materials. This requires careful study and selection of innovations that will provide future generations and raw materials with an unlimited renewable and non-pollution energy and raw materials source with advantageous side benefits.

• Asia pacific journal of information systems
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• v.9 no.1
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• pp.1-16
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• 1999

There is a controversy over the necessity of information intermediaries in virtual market. This study tried to suggest several reasons why they would still flourish in virtual market. It also tried to find out their roles and to forecast their role changes in the future on the basis of Delphi analysis. Though an exploratory study, this can shed some lights on the future studies related to information intermediaries and virtual market.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.13 no.21
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• pp.11-18
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• 1990

Because of the descent trend of the recent oil price and the ascent elements of the manufacturing price of public coal. the future demand of public coal is very obscured. In this paper, forecast the public coal demand by the regression analysis method reflected the policy and economic index of alternative energies.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.29 no.1
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• pp.479-485
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• 2005

Recently the spread which RFID technology is overcomes the limit of existing recognition technology, it is forecast with the fact that it will bring a new renovation at the business and the industrial all over. Specially the case RFID technology of Port Logistic Industry will be applied it is forecast with the fact that it will bring a many effect. The government leads introduces a RFID technology of Port Logistic Industry through the various demonstration business. But it is many with the research insufficient the depression against an actuality improvement subject and the depression of technical know-how strategy and it is difficult it is undergoing. In order to solve this problems, we propose an introduction of technical know-how Road Map that we select ranking with Existing literature investigation and the present business demand anaylsis. In the future this research it it forecast in future the successful guide line to the RFID technology introduction of Port Logistic Industry will become.

• The Journal of the Petrological Society of Korea
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• v.27 no.1
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• pp.49-66
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• 2018

Volcano hazard mapping became a focus of scientific inquiry in the 1960s. Dwight Crandell and Don Mullineaux pioneered the geologic history approach with the concept of the past is the key to the future, to hazard mapping. The 1978 publication of the Mount St. Helens hazards assessment and forecast of an eruption in the near future, followed by the large eruption in 1980 demonstrated the utility of volcano hazards assessments and triggered huge growth in this area of volcano science. Numerical models of hazardous processes began to be developed and used for identifying hazardous areas in 1980s and have proliferated since the late 1990s. Model outputs are most useful and accurate when they are constrained by geological knowledge of the volcano. Volcanic Hazard maps can be broadly categorized into those that portray long-term unconditional volcanic hazards-maps showing all areas with some degree of hazard and those that are developed during an unrest or eruption crisis and take into account current monitoring, observation, and forecast information.

• Journal of Distribution Science
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• v.17 no.10
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• pp.41-48
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• 2019

Purpose - This study investigates whether financial analysts consider the intangible investment implicit in selling, general, and administrative (SG&A) expenditures to forecast firms' future earnings. Research design, data, and methodology - Using 52,609 U.S. firm-year observations spanning 1984-2016, this study examines the association between the Intangible investment implicit in SG&A expenditures and properties of analysts' earnings forecasts. To estimate the Intangible investment of SG&A, I decompose SG&A excluding R&D and advertising expenditures into maintenance and investment components following Enache and Srivastava (2017). Results - The main results show that analysts' earnings forecast errors and dispersion in analysts' forecasts increase with the intangible investment derived from SG&A because the investment component of SG&A affects future earnings and the uncertainty of those earnings. However, these results are weakened in the wholesale and retail industries where firms have a higher level of investment component of SG&A. I attribute the weaker results to low R&D expenditures in those industries. Conclusion - This study indicates that financial analysts incorporate the intangible investment of SG&A into their earnings forecasts differently across firms and industries. Furthermore, this study supports the argument for the separate reporting of the investment nature of SG&A from other operating expenses such as maintenance nature of SG&A.

• The Korean Journal of Applied Statistics
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• v.28 no.3
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• pp.467-475
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• 2015

Accurate demand forecasting is a crucial component in revenue management(RM). The booking data of departed flights is used to forecast the demand for future departing flights; however, some booking requests that were denied were omitted in the departed flights data. Denied booking requests can be interpreted as censored in statistics. Thus, unconstraining demand is an important issue to forecast the true demands of future flights. Several unconstraining methods have been introduced and a method based on expectation maximization is considered superior. In this study, we propose a new unconstraining method based on a regression model that can entertain such censored data. Through a simulation study, the performance of the proposed method was evaluated with two representative unconstraining methods widely used in RM.

• Journal of Navigation and Port Research
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• v.35 no.4
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• pp.343-349
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• 2011

The forecasting of container volume which is the basis of port logistics facilities expansion has a great influence on development of an port. Based on this importance, various previous studies have presented methodology on container volume forecasting. The results of many previous studies pointed out the limitations of future forecasting based on past container volume and emphasized that more various factors should be considered to compensate this. Taking notice of this point, this study forecasted future container volume by using ARIMA model, time series analysis and System Dynamics (SD) method, a dynamic analysis technique and performed the comparative review with the forecast of the Ministry of Land, Transport and Maritime affairs. Recently with rapid changes in economic and social environment, the non-linear change tendency for forecasting container traffic is presented as a new alternative to the country.