• 한국태양에너지학회 논문집
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• 제25권3호
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• pp.11-18
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• 2005

The Korea Institute of Energy Research(KIER) has begun collecting horizontal global insolation data since May, 1982 at different locations. Because of a poor reliability of existing data, KIER's new data will be extensively used by the solar system users as well as by research institutes. But the quality of solar insolation data is not always good. This reports on an attempt to identify systematic error in such data using clear-day analysis for data rehabilitation. Clear-day analysis is successful in uncovering solar insolation data of questionable quality. It is not proven that rehabilitation process can improve the quality of data for daily or monthly means, but it is suggested that the method can be used to improve the quality of data for monthly means of several years for use in many applications of solar energy planning. Earlier studies finding a maximum ETR of about 0.80 are confirmed.

• 태양에너지
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• 제8권1호
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• pp.107-121
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• 1988

The Korea Institute of Energy and Resources (KIER) has begun collecting horizental insolation data since May, 1982 at different locations. Because of a poor reliability of existing data, KIER's new data will be extensively used by the solar system users as well as by research institutes. But, the quality of the solar radiation data is not always good. This reports on an attempt to identify systematic error in such data using clear-day analysis for data rehabilitation. Clear-day analysis is successful in uncovering solar radiation data of questionable quality. It is not proven that rehabilitation process can improve the quality of data for daily or monthly means but it is suggested that the method can be used to improve the quality of data for monthly means of several years for use in many applications of solar energy planning. Earlier studies finding a maximum ETR of about 0.80 are confirmed.

• 한국농공학회논문집
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• 제46권1호
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• pp.15-24
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• 2004

The purpose of this study is to estimate monthly evapotranspiration (ET) using normalized difference vegetation index (NDVI) obtained from NOAA-AVHRR data sets. Actual evapotranspiration was evaluated by the complementary relationship, and monthly NDVI was obtained by maximum value composite method from daily NDVI images in the Korean peninsula for the year 2001 The monthly actual ETs for each land cover were compared with the monthly NDVIs to determine relationships between actual ET and NDVI for each land cover category, There was a high correlation between monthly NDVI and monthly mean actual ET. This study presents an alternative approach for land surface evapotranspiration based on remote sensing techniques.

• 대한원격탐사학회:학술대회논문집
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• 대한원격탐사학회 2003년도 Proceedings of ACRS 2003 ISRS
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• pp.670-672
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• 2003

The purpose of this study is to estimate monthly evapotranspiration (ET) using normalized difference vegetation index (NDVI) obtained from NOAA/AVHRR data sets. Actual evapotranspiration was evaluated by the complementary relationship (Morton, 1978, Brutsaert and Stricker, 1979), and monthly NDVI was obtained by maximum value composite method from daily NDVI images in the Korean peninsula for the year 2001. The monthly actual ETs for each land cover were compared with the monthly NDVIs to determine relationships between actual ET and NDVI for each land cover category. There was a high correlation between monthly NDVI and monthly averaged actual ET. This study presents an alternative approach for land surface evapotranspiration based on remote sensing techniques.

• 한국조사연구학회지:조사연구
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• 제10권1호
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• pp.113-139
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• 2009

건설경기의 순환적 현상을 분석하기 위해서는 연간조사 통계자료의 속성까지 포함된 월별 벤치마킹 건설지표를 사용하는 것이 바람직할 것이다. 왜냐하면 벤치마킹된 지표는 정확성, 일치성, 비교성, 완전성 등의 측면에서 통계품질이 향상된 것으로 기대되기 때문이다. 이 논문에서는 보다 정확한 것으로 간주되는 연간 단위의 통계자료와 단기변동 특성을 보유한 월간 속보지표를 활용하여 월별 건설지표를 추정하는 벤치마킹 방법들을 연구하였다. 벤치마킹이란 연간통계와 월간통계의 일치성을 보장하면서 두 통계의 단기적 및 순환적 현상과 장기추세를 모두 보유하도록 월간통계를 조정하는 방법이다. 벤치마킹 기법으로는 수치조정 접근방법인 비례배분법, 비례덴톤법, BFL BI 비율법, HP - filter BI 비율법과 모형기반 접근방법인 Chow & Lin 방법, $Fem{\acute{a}}ndez$ 방법을 고려하였다. 또한 실제 통계자료를 가지고 벤치마킹된 건설지표를 추정하였으며, 이러한 벤치마킹 방법들을 실증적으로 비교 평가하였다. 계절적 변동 및 불규칙 변동이 심한 건설지표의 경우에 모형기반 접근방법보다는 수치조정 접근방법이 보다 우수한 것으로 평가되었다. 수치조정 접근방법으로는 가장 널리 쓰이고 있는 비례덴톤법이 무난하지만, 연간통계의 조사오차 또는 측정오차를 감안하면 HP - filter BI 비율법도 고려해 볼 수 있을 것이다.

• 한국수소및신에너지학회논문집
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• 제25권5호
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• pp.541-549
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• 2014

The monthly-average meteorological data, in particular, the monthly average daily terrestrial horizontal insolation are required for designing solar thermal energy systems. In this paper, the dynamic thermal performance of a flat plate solar collector system is numerically investigated through a year from the monthly average insolation data in Seoul. For a specified data set of solar collector system, the dynamic behaviors of total solar radiation on the tilted collector surfaces, heat loss from the collector system, useful energy and collector efficiency are analyzed from January to December by a mathematical simulation model. In addition, the monthly average daily total solar radiation, useful energy, and daily collector efficiencies through a year are estimated. The simulated results show that the average total radiation is highest in March and the useful energy is highest in October, while the total radiation and the collector efficiency are lowest in July.

• Ocean and Polar Research
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• 제24권1호
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• pp.19-31
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• 2002

Precipitation variability around King Sejong Station related with E1 $Ni\~{n}o$/Southern Oscillation (ENSO) is evaluated using the gauge-based monthly data of its neighboring stations. Though three Ant-arctic Stations of King Sejong (Korea), Frei (Chile), and Artigas (Uruguay) are all closely located within 10 km, their precipitation data show mostly insignificant positive or rather negative correlations among them in the annual, seasonal and monthly precipitation. This result indicates that there are locally large variations in the distribution of precipitation around King Sejong Station. The monthly data of Frei Station for 31 years (1970-2000) are analyzed for examining the ENSO signal in precipitation because of its longer precipitation record compared to other two stations. From the analysis of seasonal precipitation, it is seen that there is a tendency of less precipitation than the average during E1 $Ni\~{n}o$ events. This dryness is more distinct in fall to spring seasons, in which the precipitation decreases down to about 30% of seasonal mean precipitation. However, the precipitation signal related with La $Ni\~{n}a$ events is not significant. From the analysis of monthly precipitation, it is found that there is a strong negative correlation during 1980s and in the late 1990s, and a weak positive correlation in the early 1990s between normalized monthly precipitation at Frei Station and Sea Surface Temperature (SST) anomalies in the $Ni\~{n}o$ 3.4 region. However, this relation may be not applied over the region around King Sejong Station, but at only one station, Frei.

• 한국수자원학회:학술대회논문집
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• 한국수자원학회 2008년도 학술발표회 논문집
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• pp.1147-1150
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• 2008

The goal of this research is to apply the neural networks models for the disaggregation of the pan evaporation (PE) data, Republic of Korea. The neural networks models consist of the support vector machines neural networks model (SVM-NNM) and multilayer perceptron neural networks model (MLP-NNM), respectively. The SVM-NNM in time series modeling is relatively new and it is more problematic in comparison with classifications. In this study, The disaggregation means that the yearly PE data divides into the monthly PE data. And, for the performances of the neural networks models, they are composed of training, cross validation, and testing data, respectively. From this research, we evaluate the impact of the SVM-NNM and the MLP-NNM for the disaggregation of the nonlinear time series data. We should, furthermore, construct the credible data of the monthly PE data from the disaggregation of the yearly PE data, and can suggest the methodology for the irrigation and drainage networks system.

• Industrial Engineering and Management Systems
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• 제8권4호
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• pp.257-263
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• 2009

Focusing on the idea that the equation of exponential smoothing method (ESM) is equivalent to (1, 1) order ARMA model equation, new method of estimation of smoothing constant in exponential smoothing method is proposed before by us which satisfies minimum variance of forecasting error. Theoretical solution was derived in a simple way. Mere application of ESM does not make good forecasting accuracy for the time series which has non-linear trend and/or trend by month. A new method to cope with this issue is required. In this paper, combining the trend removal method with this method, we aim to improve forecasting accuracy. An approach to this method is executed in the following method. Trend removal by a linear function is applied to the original shipping data of consumer goods. The combination of linear and non-linear function is also introduced in trend removal. For the comparison, monthly trend is removed after that. Theoretical solution of smoothing constant of ESM is calculated for both of the monthly trend removing data and the non monthly trend removing data. Then forecasting is executed on these data. The new method shows that it is useful especially for the time series that has stable characteristics and has rather strong seasonal trend and also the case that has non-linear trend. The effectiveness of this method should be examined in various cases.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2004년도 ICCAS
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• pp.1330-1333
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• 2004

The methodology for modeling building energy consumption is well established for energy saving calculation in the temperate zone both for performance-based energy retrofitting contracts and measurement and verification (M&V) projects. Mostly, statistical regression models based on utility bills and outdoor dry-bulb temperature have been applied to baseline monthly and annual whole building energy use. This paper presents the application of neural networks (NN) to model landlord energy consumption of commercial buildings in Singapore. Firstly, a brief background information on NN and its application on the building energy research is provided. Secondly, five commercial buildings with various characteristics were selected for case studies. Monthly mean outdoor dry-bulb temperature ($T_0$), Relative Humidity (RH) and Global Solar Radiation (GSR) are used as network inputs and the landlord monthly energy consumption of the same period is the output. Up to three years monthly data are taken as training data. A forecast has been made for another year for all the five buildings. The performance of the NN analysis was evaluated using coefficient of variance (CV). The results show that NNs is powerful at predicting annual landlord energy consumption with high accuracy.