• Journal of Environmental Impact Assessment
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• v.16 no.1
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• pp.79-87
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• 2007

The performance of a stream water quality analysis model depends upon many factors attributed to the geological characteristics of a watershed as well as the distribution behaviors of pollutant itself on a surface of watershed. Because the model run has to import the pollution load from the watershed as a boundary condition along an interface between a stream water body and a watershed, it has been used to introduce a pollution delivery coefficient to behalf of the boundary condition of load importation. Although a nonlinear regression model (NRM) was developed to cope with the limitation of a conventional empirical way, this an up-to-date study has also a limitation that it can't be applied where the pollution load washed off (assumed at a source) is less than that delivered (observed) in a stream. The objective of this study is to identify what causes the limitation of NRM and to suggest how we can purify the process to evaluate a pollution delivery coefficient using many field observed cases. As a major result, it was found what causes the pollution load delivered to becomes bigger than that assumed at the source. In addition, the pollution load discharged to a stream water body from a specific watershed was calculated more accurately.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.58 no.1
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• pp.18-22
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• 2009

Short-term load forecasting (STLF) is an important task in power system planning and operation. Its accuracy affects the reliability and economic operation of power systems. STLF is to be classified into load forecasting for weekdays, weekends, and holidays. Due to the limited historical data available, it is more difficult to accurately forecast load for holidays than to forecast load for weekdays and weekends. It has been recognized that the forecasting errors for holidays are large compared with those for weekdays in Korea. This paper presents a polynomial regression with data mining technique to forecast load for holidays. In statistics, a polynomial is widely used in situations where the response is curvilinear, because even complex nonlinear relationships can be adequately modeled by polynomials over a reasonably small range of the dependent variables. In the paper, the coefficient of determination is proposed as a selection criterion for screening weekday data used in holiday load forecasting. A numerical example is presented to validate the effectiveness of the proposed holiday load forecasting method.

• Journal of Korean Society on Water Environment
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• v.31 no.6
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• pp.653-664
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• 2015

TPLMS (Total water pollutant load management system) that is the most powerful water-quality protection program have been implemented since 2004. In the implementation of TPLMS, target water-quality and permissible discharged load from each unit watershed can be decided by water-quality modeling. And NPS (Non-point sources) discharge coefficients associated with certain (standard) flow are used on estimation of input data for model. National Institute of Environmental Research (NIER) recommend NPS discharge coefficients as 0.15 (Q₂₇₅) and 0.50 (Q₁₈₅) in common for whole watershed in Korea. But, uniform coefficient is difficult to reflect various NPS characteristics of individual watershed. Monthly NPS discharge coefficients were predicted and estimated using surface flow and water-quality from HSPF watershed model in this study. Those coefficients were plotted in flow duration curve of study area (Palger stream and Geumho C watershed) with monthly average flow. Linear regression analysis was performed about NPS discharge coefficients of BOD, T-N and T-P associated with flow, and R² of regression were distributed in 0.893~0.930 (Palger stream) and 0.939~0.959 (Geumho C). NPS Discharge coefficient through regression can be estimated flexibly according to flow, and be considered characteristics of watershed with watershed model.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.53 no.2
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• pp.87-96
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• 2004

This paper summarizes the research results of the load management for pole transformers done in 1997-1998 and 2000-2002. The purpose of the research is to enhance the accuracy of peak load estimation in pole transformers. We concentrated our effort on the acquisition of massive actual load data for modifying the load regression coefficients, which related to the peak load estimation of lamp-use customers, and adjusting the demand-factor coefficients, which used for the peak load prediction of motor-use customers. To enhance the load regression equations, the 264 load data acquisition devices are equipped to the sample pole transformers. For the modification of demand factor coefficients, the peak load currents are measured in each customer and pole transformer for 13 KEPCO (Korea Electric Power Corporation) distribution branch offices. Case studies for 50 sample pole transformers show that the proposed coefficients could reduce estimating error of the peak load for pole transformers, compared with the conventional one.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.16 no.2
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• pp.98-103
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• 2002

In case of power demand forecasting, the most important problems are to deal with the load of special-days. Accordingly, this paper presents the method that forecasting long (the Lunar New Year, the Full Moon Festival) and short(the Planting Trees Day, the Memorial Day, etc) special-days peak load using neural networks and regression models. long and short special-days peak load forecast by neural networks models uses pattern conversion ratio and four-order orthogonal polynomials regression models. There are using that special-days peak load data during ten years(1985∼1994). In the result of special-days peak load forecasting, forecasting % error shows good results as about 1 ∼2[%] both neural networks models and four-order orthogonal polynomials regression models. Besides, from the result of analysis of adjusted coefficient of determination and F-test, the significance of the are convinced four-order orthogonal polynomials regression models. When the neural networks models are compared with the four-order orthogonal polynomials regression models at a view of the results of special-days peak load forecasting, the neural networks models which uses pattern conversion ratio are more effective on forecasting long special-days peak load. On the other hand, in case of forecasting short special-days peak load, both are valid.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2001.10a
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• pp.509-512
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• 2001

In this study, pollutant load from paddy field was estimated by regression equation from 5 to 8 in 2001. During study period, total rainfall was 511.3mm and runoff discharge was 968.71mm. Regression equation between flow rate(m3/s) and pollutant loading rate(g/s) is exponential relationship. For site 1, coefficient of determination (R2) for $COD_{cr}$, T-P, T-N were 0.7068, 0.8441, 0.6806 respectively and site 2, 0.9369, 0.8855, 0.4262 respectively. Considering unit loads, Jun was the highest valus as 13.85 $COD_{c}kg/km2/day$, 0.24 T-Pkg/km2/day, 1.22 T-Nkg/km2/day. Until study period, total $COD_{cr}$ load estimated regression equation is 19.32kg/km2/day and, T-P, T-N were 0.264, 1.88 respectively

• Magazine of the Korean Society of Agricultural Engineers
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• v.31 no.1
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• pp.106-116
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• 1989

This study aims at scrutinizing the long4errn consolidation characteristics of peats sampled at three different regions of Chonbuk province. The standard consolidation test and the single load consolidation test were performed about these samples and especially in case of the latter the loading period was 350 days. The main condusions analyzed are as follows. 1. Void ratio showed much greater values than that of the general clay and was decresed greatly according to the increase of the load. 2. In case of the relationship between the sefflement and the long-term settlement time the rate of settlement increment became great according to the increase of the load step and the long4erm settlement became linely proportional to the logarithm of time alter 10 minutes. 3. The linear correlation was showed between the long4erm settlement time and the void ratio and therefore equations by regression analysis were derived in order to estimate the long-term settlement The slope of straight lines increased according th the increase of the load step and secondary consolidation coefficients ranged from 0.04-0.27. 4. The secondary consolidation coeffcient became linealy proportional to the compression index and the ratio of Ca to CC was 0.072. 5. The period required in ending the primary consolidation was about 10 minutes and alter that the secondary consolidation coefficient appeared to have constant value. Therefore the secondary consolidation coefficient was judged to be used as a significant factor in estimating the long4erm settlement. 6. In case of the single load consolidation test the secondary consolidation coefficient showed the tendancy increasing according to the increase of the consolidation pressure.

• Journal of The Korean Society of Agricultural Engineers
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• v.60 no.1
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• pp.111-120
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• 2018

The purpose of this study was to develop a ridge regression (RR) model to estimate BOD and TP load using runoff weighted value. The concept of runoff weighted value, based on distributed curve-number (CN), was introduced to reflect the impact of land covers on runoff. The estimated runoff depths by distributed CN were closer to the observed values than those by area weighted mean CN. The RR is a technique used when the data suffers from multicollinearity. The RR model was developed for five flow duration intervals with the independent variables of daily runoff discharge of seven land covers and dependent variables of daily pollutant load. The RR model was applied to Heuk river watershed, a subwatershed of the Han river watershed. The variance inflation factors of the RR model decreased to the value less than 10. The RR model showed a good performance with Nash-Sutcliffe efficiency (NSE) of 0.73 and 0.87, and Pearson correlation coefficient of 0.88 and 0.93 for BOD and TP, respectively. The results suggest that the methods used in the study can be applied to estimate pollutant load of different land cover watersheds using limited data.

• Journal of Convergence for Information Technology
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• v.9 no.6
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• pp.1-6
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• 2019

In this study, the development of a weight estimation model of electronic scale with nonlinear characteristics is presented using polynomial regression analysis. The output voltage of the load cell was measured directly using the reference mass. And a polynomial regression model was obtained using the matrix and curve fitting function of MS Office Excel. The weight was measured in 100g units using a load cell electronic scale measuring up to 5kg and the polynomial regression model was obtained. The error was calculated for simple($1^{st}$), $2^{nd}$ and $3^{rd}$ order polynomial regression. To analyze the suitability of the regression function for each model, the coefficient of determination was presented to indicate the correlation between the estimated mass and the measured data. Using the third order polynomial model proposed here, a very accurate model was obtained with a standard deviation of 10g and the determinant coefficient of 1.0. Based on the theory of multi regression model presented here, it can be used in various statistical researches such as weather forecast, new drug development and economic indicators analysis using logistic regression analysis, which has been widely used in artificial intelligence fields.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.4
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• pp.365-370
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• 1999

This paper describes the Weekly Load Forecasting Expert System(Named WLoFy) which was developed and implemented for Korea Electric Power Corporation(KEPCO). WLoFy was designed to provide user oriented features with a graphical user interface to improve the user interaction. The various forecasting models such as exponential smoothing, multiple regression, artificial nerual networks, rult-based model, and relative coefficient model also have been included in WLofy to increase the forecasting accuracy. The simulation based on historical data shows that the weekly forecasting results form WLoFy is an improvement when compared to the results from the conventional methods. Especially the forecasting accuracy on special days has been improved remakably.