• Korean Journal of Environmental Biology
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• v.24 no.2 s.62
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• pp.160-165
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• 2006

The purpose of this study was to ascertain the prey composition and utilization of the Eurasian otter, Lutra lutra, in the ecosystem conservation region of the Seomjin River. The study was conducted for a 12-month period from March 2003 to February 2004 by spraints analysis and an investigation using artificial holts. We found seasonal variation in the number of spraints and composition of prey items. Fish were the main prey items throughout the period (82%), and other prey items included birds (14%), amphibians (2%) and arthropods (1%). In addition to prey identification, we investigated habitat utilization by otters through spraints distribution. The traces of otters were discovered in five of the six total artificial holts. However, the utilization of the artificial holts in the study area seemed to be limited as evidenced by the low frequency of traces in the holts.

• The Korean Journal of Applied Statistics
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• v.31 no.1
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• pp.139-153
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• 2018

This paper investigates solar power forecasting based on several time series models. First, we consider weather variables that influence forecasting procedures as well as compare forecasting accuracies between time series models such as ARIMAX, Holt-Winters and Artificial Neural Network (ANN) models. The results show that ten models forecasting 24hour data have better performance than single models for 24 hours.

• Journal of Electrical Engineering and Technology
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• v.10 no.3
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• pp.832-837
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• 2015

The group method of data handling (GMDH) algorithm has proven to be a powerful and effective way to extract rules or polynomials from an electric load pattern. However, because it is nonstationary, the load pattern needs to be decomposed using a discrete wavelet transform. In addition, if a load pattern has a complicated curve pattern, GMDH should use a higher polynomial, which requires complex computing and consumes a lot of time. This paper suggests a method for short-term electric load forecasting that uses a wavelet transform and a GMDH algorithm. Case studies with the proposed algorithm were carried out for one-day-ahead forecasting of hourly electric loads using data during the years 2008-2011. To prove the effectiveness of our proposed approach, the results were evaluated and compared with those obtained by Holt-Winters method and artificial neural network. Our suggested method resulted in better performance than either comparison group.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.300-301
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• 2015

Accurate and robust load forecasting model plays very important role in power system operation. In case of short-term electric load forecasting, its results offer standard to decide a price of electricity and also can be used shaving peak. For this reason, various models have been developed to improve accuracy of load forecasting. This paper proposes a newly forecasting model for weather sensitive season including temperature and Cooling Degree Hour(C.D.H) data as an input. This Forecasting model consists of previous electric load and preprocessed temperature, constant, parameter. It optimizes load forecasting model to fit actual load by PSO and results are compared to Holt-Winters and Artificial Neural Network. Proposing method shows better performance than comparison groups.

• 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.