• Journal of Korean Society of Forest Science
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• 제97권4호
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• pp.458-466
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• 2008

This study was carried out to forecast long-term supply and demand of chestnut and to analyze the impacts of change in the environment of domestic and international chestnut markets. For these ends, the study developed a partial equilibrium market model, in which in-shelled chestnut market was vertically linked to shelled chestnut market. To examine the predictive ability of the model for the endogenous variables ex-post simulation was run for the period 1990 through 2003. In general, all endogenous variables reproduced the historical trends during the period except for disuse areas and newly established areas. The results of forecasting supply and demand show that domestic in-shelled chestnut production is estimated to decrease slightly from 76,447 ton in 2005 to 76,286 ton in 2020 and that exports of shelled chestnut continue to be decreased.

• The Transactions of The Korean Institute of Electrical Engineers
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• 제65권9호
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• pp.1486-1492
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• 2016

This study presents the electrical load forecasting and error correction method using a real building load pattern, and the way to manage the energy storage system with forecasting results for economical load operation. To make a unique pattern of target load, we performed the Hierarchical clustering that is one of the data mining techniques, defined load pattern(group) and forecasted the demand load according to the clustering result of electrical load through the previous study. In this paper, we propose the new reference demand for improving a predictive accuracy of load demand forecasting. In addition we study an error correction method for response of load events in demand load forecasting, and verify the effects of proposed correction method through EMS scheduling simulation with load forecasting correction.

• Korean Journal of Construction Engineering and Management
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• 제7권2호
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• pp.162-170
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• 2006

Neural network analysis is expected to enhance the forecasting ability for the real estate market. This paper reviews definition, structure, strengths and weaknesses of neural network analysis, and verifies the applicability of neural network analysis for the real estate market. Neural network analysis is compared with regression analysis using the same sample data. The analyses model the macroeconomic parameters that influence the sales price of apartments. The results show that neural network analysis provides better forecasting accuracy than regression analysis does, what confirms the applicability of neural network analysis for the real estate market.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• 제17권6호
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• pp.54-59
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• 2003

This paper presents hourly system marginal price forecasting of the Korea electric power system using a fuzzy linear regression analysis method. The proposed method is tested by forecasting hourly system marginal price for a week of spring in 2002. The percent average of forecasting error for the proposed method is from 3.14% to 6.10% in the weekdays, from 7.04% to 8.22% in the weekends, and comparable with a artificial neural networks method.

• Journal of Electrical Engineering and Technology
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• 제5권3호
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• pp.363-370
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• 2010

Load forecasting has always been essential to the operation and planning of power systems in deregulated electricity markets. Various methods have been proposed for load forecasting, and the neural network is one of the most widely accepted and used techniques. However, to obtain more accurate results, more information is needed as input variables, resulting in huge computational costs in the learning process. In this paper, to reduce training time in multi-layer perceptron-based short-term load forecasting, a graphics processing unit (GPU)-based computing method is introduced. The proposed approach is tested using the Korea electricity market historical demand data set. Results show that GPU-based computing greatly reduces computational costs.

• KIEE International Transactions on Power Engineering
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• 제4A권3호
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• pp.159-166
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• 2004

Forecasting prices in electricity markets is critical for consumers and producers in planning their operations and managing their price risk. We utilize the generalized autoregressive conditionally heteroskedastic (GARCH) method to forecast the electricity prices in two regions of New York： New York City and Central New York State. We contrast the one-day forecasts of the GARCH against techniques such as dynamic regression, transfer function models, and exponential smoothing. We also examine the effect on our forecasting of omitting some of the extreme values in the electricity prices. We show that accounting for the extreme values and the heteroskedactic variance in the electricity price time-series can significantly improve the accuracy of the forecasting. Additionally, we document the higher volatility in New York City electricity prices. Differences in volatility between regions are important in the pricing of electricity options and for analyzing market performance.

• The Journal of Asian Finance, Economics and Business
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• 제9권3호
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• pp.181-193
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• 2022

This paper seeks to investigate major macroeconomic factors and bond yield interactions in Thai bond markets, with the goal of forecasting future bond yields. This study examines the best predictive yields for future bond yields at different maturities of 1-, 3-, 5-, 7-, and 10-years using time series data of economic indicators covering the period from 1998 to 2020. The empirical findings support the hypothesis that macroeconomic factors influence bond yield fluctuations. In terms of forecasting future bond yields, static predictions reveal that in most cases, the BVAR model offers the best predictivity of bond rates at various maturities. Furthermore, the BVAR model has the best performance in dynamic rolling-window, forecasting bond yields with various maturities for 2-, 4-, and 8-quarters. The findings of this study imply that the BVAR model forecasts future yields more accurately and consistently than other competitive models. Our research could help policymakers and investors predict bond yield changes, which could be important in macroeconomic policy development.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 한국경영과학회 2000년도 추계학술대회 및 정기총회
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• pp.77-80
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• 2000

Feedback herding strategy in stock market means considering other investor's strategy as a basis of market forecasting of next term. Generally, individual investors use that strategy which mimics the strategy of institutional investors. When it is used in stock market, both kind of investors, preceders and followers, can take the higher average of rate of return to normal market in which no feedback herding strategy is not use, the more investors take part in. And variance of return, the risk of investment, are same to both group.

• Journal of Electrical Engineering and Technology
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• 제12권6호
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• pp.2177-2186
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• 2017

The goal of this paper is to provide the specific forecasting steps and to explain how to design the forecasting architecture and training data sets to forecast very short-term wind power when the numerical weather prediction (NWP) is unavailable, and when the sampling periods of the wind power and training data are different. We forecast the very short-term wind power every 15 minutes starting two hours after receiving the most recent measurements up to 40 hours for a total of 38 hours, without using the NWP data but using the historical weather data. Generally, the NWP works as a predictor and can be converted to wind power forecasts through machine learning-based forecasting algorithms. Without the NWP, we can still build the predictor by shifting the historical weather data and apply the machine learning-based algorithms to the shifted weather data. In this process, the sampling intervals of the weather and wind power data are unified. To verify our approaches, we participated in the 2017 wind power forecasting competition held by the European Energy Market conference and ranked sixth. We have shown that the wind power can be accurately forecasted through the data shifting although the NWP is unavailable.

• The Journal of Society for e-Business Studies
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• 제24권2호
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• pp.199-216
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• 2019

The public SW market is 3 trillion won, which is less than 10% of the total SW market. However, due to the nature of the domestic market, it is an important market with a relatively large impact on small and medium-sized software companies. In this market, government is operating the Public SW Project Demand Forecasting System in order to support the marketing activities of small and medium sized SW companies and establish a fair market order. The current system has limitations such as lack of user convenience, insufficient analysis capability and less business connection. This study was conducted to identify the problems of these systems and to propose a new system for improving the convenience of users and expanding the information utilization of SMEs. To this end, we analyzed the requirements of each stakeholder. We proposed the 2-phased forecasting cycle, the management cycle, and the system life cycle of public SW projects and created a unified identifier (UID) so that the information of those projects can be identified and linked among them. As a result, an integrated reference model of project information management based on system life cycle was developed, which can explain the demand forecasting and project information, and the improved processes was also designed to implement them. Through the result of this study, it is expected that integrated management of public SW projects will be possible.