• Journal of the Ergonomics Society of Korea
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• v.26 no.1
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• pp.79-85
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• 2007

Demand forecasting methods for a consumer product such as TV or refrigerator are widely known. However, sales forecast for a brand new product cannot be estimated using conventional forecasting methods. This study proposes a five-step procedure in forecasting a newly developed product. Step one defines functions in a High Touch product in order to estimate relative attraction of the product to consumer group. In step two, for a comparison purpose, a compatible product that is successfully penetrated into market is selected. Step three breaks a target population into many segments based on demography. Step four calculates relative attraction between the High Touch product and the compatible product. Finally, market penetration rate of the High Touch product is estimated using a bell-shaped diffusion curve of the compatible product. The process offers a method to estimate potential demand and growth pattern of the new High Touch product.

• Asia Pacific Journal of Business Review
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• v.5 no.1
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• pp.1-9
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• 2020

Tourism development stimulates job creation and the development of other sectors of the economy. More than 30 sectors of the economy are connected to tourism. It distributes resources between sectors and stimulates of development of such sectors like transport, communications, services, trade, construction, and the production of consumer goods. All these increase the importance of tourism as well as forecasting it by analyzing the demand. This study is a review on inbound tourism of Uzbekistan. The study will examine regression analysis as an effective tool that plays an important role as well as in the field of tourism demand analysis. In this study, firstly the estimating tourism demand is explained, secondly, the regression analysis is examined as an estimating tool of tourism demand. The paper includes a country study dedicated to the Tourism market of Uzbekistan. Nevertheless, the forecast on the inbound tourism of Uzbekistan was developed by using some statistical data.

• Proceedings of the Korea Water Resources Association Conference
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• 2007.05a
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• pp.1910-1914
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• 2007

This paper employs a relatively new technique of Artificial Neural Network (ANN) to forecast water demand of Daegu city. The ANN model used in this study is a single hidden layer hierarchy model. About seventeen sets of historical water demand records and the values of their socioeconomic impact factors are used to train the model. Also other regression and time serious models are investigated for comparison purpose. The results present the ANN model can better perform the issue of urban water demand forecasting, and obtain the correlation coefficient of $R^2$ with a value of 0.987 and the relative difference less than 4.4% for this study.

• Korean System Dynamics Review
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• v.9 no.1
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• pp.125-141
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• 2008

It is very difficult to predict future demand/supply, price for resources with acceptable accuracy using regression analysis. We try to use system dynamics to forecast the demand/supply and price for nickel. Nickel is very expensive mineral resource used for stainless production or other industrial production like battery, alloy making. Recent nickel price trend showed non-linear pattern and we anticipated the system dynamic method will catch this non-linear pattern better than the regression analysis. Our model has been calibrated for the past 6 year quarterly data (2002-2007) and tested for next 5 year quarterly data(2008-2012). The results were acceptable and showed higher accuracy than the results obtained from the regression analysis. And we ran the simulations for scenarios made by possible future changes in demand or supply related variables. This simulations implied some meaningful price change patterns.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.9
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• pp.1695-1699
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• 2009

This paper analyzed the characteristics of the demand of electric power in Jeju by year, day. For this analysis, this research used the correlation between the changes in the temperature and the demand of electric power in summer, and cleaned the data of the characteristics of the temperatures, using the coefficient of correlation as the standard. And it proposed the algorithm of forecasting the short-term electric power demand in Jeju, Therefore, in the case of summer, the data by each cleaned temperature section were used. Based on the data, this paper forecasted the short-term electric power demand in the exponential smoothing method. Through the forecast of the electric power demand, this paper verified the excellence of the proposed technique by comparing with the monthly report of Jeju power system operation result made by Korea Power Exchange-Jeju.

• The Journal of the Korea Contents Association
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• v.19 no.1
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• pp.132-140
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• 2019

Recently, many cities around the world introduced and operated shared bicycle system to reduce the traffic and air pollution. Seoul also provides shared bicycle service called as "Ddareungi" since 2015. As the use of shared bicycle increases, the demand for bicycle in each station is also increasing. In addition to the restriction on budget, however, there are managerial issues due to the different demands of each station. Currently, while bicycle rebalancing is used to resolve the huge imbalance of demands among many stations, forecasting uncertain demand at the future is more important problem in practice. In this paper, we develop forecasting model for demand for Seoul shared bicycle using statistical time series analysis and apply our model to the real data. In particular, we apply Holt-Winters method which was used to forecast electricity demand, and perform sensitivity analysis on the parameters that affect on real demand forecasting.

• Journal of IKEEE
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• v.23 no.1
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• pp.127-133
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• 2019

Electric power demand forecasting is one of the important research areas for future smart grid introduction. However, It is difficult to predict because it is affected by many external factors. Traditional methods of forecasting power demand have been limited in making accurate prediction because they use raw power data. In this paper, a probability-based CRBM is proposed to solve the problem of electric power demand prediction using raw power data. The stochastic model is suitable to capture the probabilistic characteristics of electric power data. In order to compare the mid-term power demand forecasting performance of the proposed model, we compared the performance with Recurrent Neural Network(RNN). Performance comparison using electric power data provided by the University of Massachusetts showed that the proposed algorithm results in better performance in mid-term energy demand forecasting.

• Journal of Korean Society of Water and Wastewater
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• v.37 no.6
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• pp.363-373
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• 2023

Numerous studies have established a correlation between sociodemographic characteristics and water usage, identifying population as a primary independent variable in mid- to long-term demand forecasting. Recent dramatic sociodemographic changes, including urban concentration-rural depopulation, low birth rates-aging population, and the rise in single-person households, are expected to impact water demand and supply patterns. This underscores the necessity for operational and managerial changes in existing water supply systems. While sociodemographic characteristics are regularly surveyed, the conducted surveys use aggregate units that do not align with the actual system. Consequently, many water demand forecasts have been conducted at the administrative district level without adequately considering the water supply system. This study presents an upward water demand forecasting model that accurately reflects real water facilities and consumers. The model comprises three key steps. Firstly, Statistics Korea's SGIS (Statistical Geological Information System) data was reorganized at the DMA level. Secondly, DMAs were classified using the SOM (Self-Organizing Map) algorithm to consider differences in water facilities and consumer characteristics. Lastly, water demand forecasting employed the PCR (Principal Component Regression) method to address multicollinearity and overfitting issues. The performance evaluation of this model was conducted for DMAs classified as rural areas due to the insufficient number of DMAs. The estimation results indicate that the correlation coefficients exceeded 0.9, and the MAPE remained within approximately 10% for the test dataset. This method is expected to be useful for reorganization plans, such as the expansion and contraction of existing facilities.

• Journal of Information Technology Services
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• v.14 no.4
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• pp.185-197
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• 2015

As the uncertainty of demand in B2B electronics market has increased, firms need a strong method to estimate the market demand. An accurate prediction on the market demand is crucial for a firm not to overproduce or underproduce its goods, which would influence the performance of the firm. However, it is complicated to estimate the demand in a B2B market, particularly for the private sector, because firms are very diverse in terms of size, industry, and types of business. This study proposes both qualitative and quantitative demand forecasting approaches for B2B PC products. Four different measures for predicting PC products in B2B market with consideration of the different PC uses-personal work, common work, promotion, and welfare-are developed as the qualitative model's input variables. These measures are verified by survey data collected from experts in 139 firms, and can be applied when individual firms estimate the demand of PC goods in a B2B market. As the quantitative approach, the multiple regression model is proposed and it includes variables of region, type of industry, and size of the firm. The regression model can be applied when the aggregated demand for overall domestic PC market needs to be estimated.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.10
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• pp.126-133
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• 2007

This paper proposes information technology based data mining to forecast short term power demand. A time-series analyses have been applied to power demand forecasting, but this method needs not only heavy computational calculation but also large amount of coefficient data. Therefore, it is hard to analyze data in fast way. To overcome time consuming process, the author take advantage of universally easily available information technology based data-mining technique to analyze patterns of days and special days(holidays, etc.). This technique consists of two steps, one is constructing decision tree, the other is estimating and forecasting power flow using decision tree analysis. To validate the efficiency, the author compares the estimated demand with real demand from the Korea Power Exchange.