• The Journal of Fisheries Business Administration
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• v.15 no.1
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• pp.114-130
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• 1984

The structure of food demand is being changed according to the improvement of living standard. Moreover, the intake of animal protein is stepping up. This paper considers how much fresh-fish is consumed as source of animal protein and what extent fresh-fish have substitutive relation for meat with special reference to the change of income and price of fresh-fish and meat. And it is thought to be important work to estimate demand of fresh-fish in attemps to the prediction of food consume pattern and fishing industries in the future. For this estimation, the substitutive relation of fresh-fish and meat is essentially studied. The main conclusions of this study can be drawn as follows: 1. Fresh-fish and meat have substitutive relation on price axis. By the way, increase in demand of A (fresh-fish which have comparatively low price) can be expected according to the low of it's price against meat, but B (fresh-fish wihich have comparatively middle-high price) have peculiar demand without substitutive relation for meat. 2. Demand of A and B rise according to the income increases. 3. It is not sufficient to explain substutive relation of fresh-fish and meat without income variable. 4. Income increases bring about the more increase in demand of B than A. By the way, price increases bring about the decrease of it's consume expenditure, but A have fundamental demand as the source of animal protein. 5. In future, the intake of animal protein will step up. By the way, meat will occupy the more portion of the source of animal protein than fresh-fish.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.1
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• pp.50-58
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• 2018

Intermittent demand is a demand with a pattern in which zero demands occur frequently and non-zero demands occur sporadically. This type of demand mainly appears in spare parts with very low demand. Croston's method, which is an initiative intermittent demand forecasting method, estimates the average demand by separately estimating the size of non-zero demands and the interval between non-zero demands. Such smoothing type of forecasting methods can be suitable for mid-term or long-term demand forecasting because those provides the same demand forecasts during the forecasting horizon. However, the smoothing type of forecasting methods aims at short-term forecasting, so the estimated average forecast is a factor to decrease accuracy. In this paper, we propose a forecasting method to improve short-term accuracy by improving Croston's method for intermittent demand forecasting. The proposed forecasting method estimates both the non-zero demand size and the zero demands' interval separately, as in Croston's method, but the forecast at a future period adjusted by binomial weight according to occurrence probability. This serves to improve the accuracy of short-term forecasts. In this paper, we first prove the unbiasedness of the proposed method as an important attribute in forecasting. The performance of the proposed method is compared with those of five existing forecasting methods via eight evaluation criteria. The simulation results show that the proposed forecasting method is superior to other methods in terms of all evaluation criteria in short-term forecasting regardless of average size and dispersion parameter of demands. However, the larger the average demand size and dispersion are, that is, the closer to continuous demand, the less the performance gap with other forecasting methods.

• Journal of the Korean Data and Information Science Society
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• v.26 no.4
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• pp.885-894
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• 2015

It is necessary to forecast the electricity demand for reliable and effective operation of the power system. In this study, we try to categorize a functional data, the mean curve in accordance with the time of daily power demand pattern. The data were collected between January 1, 2009 and December 31, 2011. And it were converted to time series data consisting of seasonal components and error component through log transformation and removing trend. Functional clustering by Ma et al. (2006) are applied and parameters are estimated using EM algorithm and generalized cross validation. The number of clusters is determined by classifying holidays or weekdays. Monday, weekday (Tuesday to Friday), Saturday, Sunday or holiday and season are described the mean curve of daily power demand pattern.

• The Journal of the Korea Contents Association
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• v.15 no.6
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• pp.399-410
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• 2015

The primary goal of the study is to examine the possibility of applying the time series model to forecasting demand and supply of social services. In the study, we used survey data based on a nationally represented sample which is secondary processed data. We selected developmental rehabilitation service. The analysis, we made models of a demand and a supply using time series analysis. Utilizing the estimates, we identified each model's pattern. This study provides an empirical evidence to suggest benefits of using the time series model for forecasting the demand and the supply pattern of newly introduced social services. We also provide discussions on policy implications of utilizing demand and supply time series models in the process of developing new social services.

• Journal of the Korean Data and Information Science Society
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• v.28 no.2
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• pp.395-406
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• 2017

The purpose of this study is to identify the pattern of daily electricity demand through clustering and classification. The hourly data was collected by KPS (Korea Power Exchange) between 2008 and 2012. The time trend was eliminated for conducting the pattern of daily electricity demand because electricity demand data is times series data. We have considered k-means clustering, Gaussian mixture model clustering, and functional clustering in order to find the optimal clustering method. The classification analysis was conducted to understand the relationship between external factors, day of the week, holiday, and weather. Data was divided into training data and test data. Training data consisted of external factors and clustered number between 2008 and 2011. Test data was daily data of external factors in 2012. Decision tree, random forest, Support vector machine, and Naive Bayes were used. As a result, Gaussian model based clustering and random forest showed the best prediction performance when the number of cluster was 8.

• The Journal of Fisheries Business Administration
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• v.34 no.2
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• pp.53-73
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• 2003

This paper is to analyze the past and present consumption pattern of fishes aquacultured in marine waters and thus to draw the policy direction to enhance the competitiveness of marine fish aquaculture in Korea. At present, the volume of meat consumption is surveyed to be more than that of marine fish, but it is revealed that fish consumption will become more increasing in the future according to the rise of the income. The survey shows that the consumption of fish is highest in the fall, and among the various patterns of consumption, live fish, so-called susi, is surveyed to be highly dominant. It is revealed that fish is enjoyed because of the special savor, diverse nutrients, and the prevention of adult diseases. Natural fish Is revealed to be more preferred to aquacultured one due to the sticky flesh quality and the low probability of the remained after the production process antibiotics, so that it is necessary to enhance the taste quality and make a clean cultivation to capture more market demand. Consumption of high-quality fish seems to become high in more than middle class and consumption of fish are estimated to increase in the future, more than that of meat if income level of the people increases. Also, if we try to make our high-quality fish become popular among the public and competible with the imported fish from abroad, it is recommended that they must lower production price by cost reduction and try to differentiate it by taste and environmental safety, etc. It was revealed that the significant factor in demand function for fish is income and it is almost the only factor affecting that demand. Also, it was revealed that the most significant factor affecting preference of fish is income and it Is almost the only factor affecting the preference. Therefore, we can ascertain that if proper goods can be distributed, demand for and preference of fish may increase according to the increase of income in the future.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.4
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• pp.587-594
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• 2018

The purpose of this study is to propose a power demand volatility evaluation system based on LSTM and not to verify the accuracy of the demand module which is a core module, but to recognize the sudden change of power pattern by using deeplearning in the actual power demand monitoring system. Then we confirm the availability of the module. Also, we tried to provide a visualized report so that the manager can determine the fluctuation of the power usage patten by applying it as a module to the web based system. It is confirmed that the power consumption data shows a certain pattern in the case of government offices and hospitals as a result of implementation of the volatility evaluation system. On the other hand, in areas with relatively low power consumption, such as residential facilities, it was not appropriate to evaluate the volatility.

• Korean Journal of Community Nutrition
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• v.8 no.6
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• pp.910-918
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• 2003

The purposes of this study were to develop a model for university foodservices and to provide management strategies for reducing costs, and increasing productivity and customer satisfaction. The results of this study were as follows : 1) The demands in university food services varied depending on the time series. A fixed pattern was discovered for specific times of the month and semesters. The demand tended to constantly decrease from the beginning of a specific semester to the end, from March to June and from September to December. Moreover, the demand was higher during the first semester than the second semester, within school term than during vacation periods, and during the summer vacation than the winter. 2) Pearson's simple correlation was done between actual customer demand and the factors relating to forecasting the demand. There was a high level of correlation between the actual demand and the demand that had occurred in the previous weeks. 3) By applying the stepwise multiple linear regression analysis to two different university food services providing multiple menu items, a model was developed in terms of four different time series(first semester, second semester, summer vacation, and winter vacation). Customer preference for specific menu items was found to be the most important factor to be considered in forecasting the demand.

• Proceedings of the Korea Society for Simulation Conference
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• 2002.11a
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• pp.101-105
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• 2002

In a traditional hierarchical inventory system, direct orders are the only information for inventory management that is exchanged between the firms involved. But due to the rapid development of modern information technology, it becomes possible for the firms to share more information in real time, e.g. demand and inventory status data. And so the term Supply Chain has emerged because it is seen as an important source of competitive advantage. Now it is possible to challenge traditional approaches to inventory management. In the past, one of the de-facto assumptions for inventory management was that the demand pattern follows a specific distribution function. However, it is undesirable to apply this assumption in real situations because the demand information in the supply chain tends to be distorted due to the bullwhip effect in a supply chain. To overcome this weakness, we propose a new solution method using NN (Neural Network). Our method proceeds in three steps. First, we find the patterns of optimal reorder points by analyzing past data. Second. train the NN using these pattern data and finally decide the reorder point. Using simulation experiment, we show that the proposed solution method gives better result than that of traditional research.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.12
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• pp.1765-1773
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• 2012

The accuracy of load forecast is very important from the viewpoint of economical power system operation. In general, the weekdays' load demand pattern has the continuous time series characteristics. Therefore, the conventional methods expose stable performance for weekdays. In case of special days or weekends, the load demand pattern has the discontinuous time series characteristics, so forecasting error is relatively high. Especially, weekdays near the thanksgiving day and lunar new year's day have the mixed load profile characteristics of both weekdays and weekends. Therefore, it is difficult to forecast these days by using the existing algorithms. In this study, a new load forecasting method is proposed in order to enhance the accuracy of the forecast result considering the characteristics of weekdays and weekends. The proposed method was tested with these days during last decades, which shows that the suggested method considerably improves the accuracy of the load forecast results.