• Journal of Internet Computing and Services
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• v.18 no.1
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• pp.121-129
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• 2017

Demand forecasting is one of the most critical tasks in defense logistics, because the failure of the task can bring about a huge waste of budget. Up to date, ROK-MND(Republic of Korea - Ministry of National Defense) has analyzed past component consumption data with time-series techniques to predict each component's demand. However, the accuracy of the prediction still needs to be improved. In our study, we attempted to find consumption pattern using data mining techniques. We gathered an 18,476 component consumption data first, and then derived diverse features to utilize them in identification of demanding patterns in the consumption data. The results show that our approach improves demand forecasting with higher accuracy.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.49 no.3
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• pp.107-117
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• 2000

This paper proposes a systematic method to develop short-term electrical load forecasting systems using neuro-fuzzy models. The primary goal of the proposed method is to improve the performance of the prediction model in terms of accuracy and reliability. For this, the proposed method explores the advantages of the structure learning of the neuro-fuzzy model. The proposed load forecasting system first builds an initial structure off-line for each hour of four day types and then stores the resultant initial structures in the initial structure bank. Whenever a prediction needs to be made, the proposed system initializes the neuro-fuzzy model with the appropriate initial structure stored and trains the initialized model. In order to demonstrate the viability of the proposed method, we develop an one hour ahead load forecasting system by using the real load data collected during 1993 and 1994 at KEPCO. Simulation results reveal that the prediction system developed in this paper can achieve a remarkable improvement on both accuracy and reliability compared with the prediction systems based on multilayer perceptrons, radial basis function networks, and neuro-fuzzy models without the structure learning.

• The Korean Journal of Financial Management
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• v.20 no.1
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• pp.93-124
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• 2003

This paper studies the financial analyst's forecasting activities on the firm's operating performance during the period from 1999 to 2003. In this study, financial analyst's forecasting activities are focused on the sales, operating income and net income and financial analyst's forecasting accuracy, forecasting revising patterns and forecasting activities to the unexpected firm's operating performance are studied. Some empirical findings in this study are as follows. First, standard estimate error on the sales, operating income and net income are all significantly negative value and so financial analyst's forecast on the firm's operating performance are upwardly biased. Second, domestic financial analyst's forecasting activities is relatively more accuracy than foreign financial analyst's forecasting activities. Third, forecasting time is more close to the end of the operating performance announcement day, forecasting activities are more accuracy. Fourth, comparing with individual financial analyst's forecast, consensus forecast is more accuracy. Fifth, in the comparative forecasting activities study according to the prior firm's operating performance, financial analyst's forecasting revision activities are found to be upward or downward. Sixth, financial analysts overreact in the sales forecast and underreact in the operating income and net income forecast. Seventh, in the empirical analysis on the Easterwood-Nutt's test model(1999) which the firm's performance change are divided into the expected performance change and the unexpected performance change, it is found that financial analyst's forecasting activities on the firm's operating performance are systematically optimistic.

• Korean Journal of Construction Engineering and Management
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• v.4 no.4 s.16
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• pp.114-122
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• 2003

Dynamic and fragmented characteristics are two of the most significant factors that distinguish the construction industry from other industries. Previous forecasting techniques have failed to solve the problems derived from the above characteristics, and do not provide considerable support This paper deals with providing a more precise forecasting by applying Case-based Reasoning (CBR). The newly developed model in this study enables project managers to forecast monthly expenditures with less time and effort by retrieving and referring only projects of a similar nature, while filtering out irrelevant cases included in database. For the purpose of accurate forecasting, the choice of the numbers of referring projects was investigated. It is concluded that selecting similar projects at $5{\~}6{\%}$ out of the whole database will produce a more precise forecasting. The new forecasting model, which suggests the predicted values based on previous projects, is more than just a forecasting methodology; it provides a bridge that enables current data collection techniques to be used within the context of the accumulated information. This will eventually help all the participants in the construction industry to build up the knowledge derived from invaluable experience.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.9
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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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.64 no.8
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• pp.1137-1144
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• 2015

Accurate and robust load forecasting model is very important in power system operation. In case of short-term electric load forecasting, its result is offered as an standard to decide a price of electricity and also can be used shaving peak. For this reason, various models have been developed to improve forecasting accuracy. In order to achieve accurate forecasting result for summer season, this paper proposes a forecasting model using corrected effective temperature based on Heat Index and CDH data as inputs. To do so, we establish polynomial that expressing relationship among CDH, load, temperature. After that, we estimate parameters that is multiplied to each of the terms using PSO algorithm. The forecasting results are compared to Holt-Winters and Artificial Neural Network. Proposing method shows more accurate by 1.018%, 0.269%, 0.132% than comparison groups, respectively.

• Journal of Electrical Engineering and Technology
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• v.13 no.1
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• pp.38-50
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• 2018

Spatio-temporal load forecasting (STLF) is a foundation for building the prediction-based power map, which could be a useful tool for the visualization and tendency assessment of urban energy application. Constructing one point-forecasting model for each electricity cell in the geographic space is possible; however, it is unadvisable and insufficient, considering the aggregation features of electricity cells and uncertainties in input variables. This paper presents a new STLF method, with a data-driven framework consisting of 3 subroutines: multi-level clustering of cells considering their aggregation features, load regression for each category of cells based on SLS-SVRNs (sparse least squares support vector regression networks), and interval forecasting of spatio-temporal load with sampled blind number. Take some area in Pudong, Shanghai as the region of study. Results of multi-level clustering show that electricity cells in the same category are clustered in geographic space to some extent, which reveals the spatial aggregation feature of cells. For cellular load regression, a comparison has been made with 3 other forecasting methods, indicating the higher accuracy of the proposed method in point-forecasting of spatio-temporal load. Furthermore, results of interval load forecasting demonstrate that the proposed prediction-interval construction method can effectively convey the uncertainties in input variables.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.1
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• pp.8-14
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• 1999

This paper presents the methods of short-term load forecasting Kohonen neural networks and back-propagation neural networks. First, historical load data is divided into 5 patterns for the each seasonal data using Kohonen neural networks and using these results, load forecasting neural network is used for next day hourly load forecasting. Next day hourly load of weekdays and weekend except holidays are forecasted. For load forecasting in summer, max-temperature and min-temperature data as well as historical hourly load date are used as inputs of load forecasting neural networks for a better forecasting accuracy. To show the possibility of the proposed method, it was tested with hourly load data of Korea Electric Power Corporation(1994-95).

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.7
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• pp.1144-1150
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• 2016

Short-term load forecasting is essential to the electricity pricing and stable power system operations. The conventional weekday 24-hour load forecasting algorithms consider the temperature model to forecast maximum load and minimum load. But 24-hour load pattern forecasting models do not consider temperature effects, because hourly temperature forecasts were not present until the latest date. Recently, 3 hour temperature forecast is announced, therefore hourly temperature forecasts can be produced by mathematical techniques such as various interpolation methods. In this paper, a new 24-hour load pattern forecasting method is proposed by using similar day search considering the hourly temperature. The proposed method searches similar day input data based on the anomalous weather features such as continuous temperature drop or rise, which can enhance 24-hour load pattern forecasting performance, because it uses the past days having similar hourly temperature features as input data. In order to verify the effectiveness of the proposed method, it was applied to the case study. The case study results show high accuracy of 24-hour load pattern forecasting.

• Korean Journal of Agricultural Science
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• v.45 no.4
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• pp.859-870
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• 2018

Dried red peppers are a staple agricultural product used in Korean cuisine and as such, are an important aspect of agricultural producers' income. Correctly forecasting both their supply and demand situations and price is very important in terms of the producers' income and consumer price stability. The primary objective of this study was to compare the performance of time series forecasting models for dried red peppers in Korea. In this study, three models (an autoregressive model with exogenous variables [ARX], AR-exponential generalized autoregressive conditional heteroscedasticity [EGARCH], and ARX-EGARCH) are presented for forecasting the wholesale price of dried red peppers. As a result of the analysis, it was shown that the ARX model and ARX-EGARCH model, each of which adopt both the rolling window and the adding approach and use the agricultural cooperatives price as the exogenous variable, showed a better forecasting performance compared to the autoregressive model (AR)-EGARCH model. Based on the estimation methods and results, there was no significant difference in the accuracy of the estimation between the rolling window and adding approach. In the case of dried red peppers, there is limitation in building the price forecasting models with a market-structured approach. In this regard, estimating a forecasting model using only price data and identifying the forecast performance can be expected to complement the current pricing forecast model which relies on market shipments.