• Journal of Environmental Science International
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• 제15권7호
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• pp.653-657
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• 2006

Based on the monthly weather report of Korea Meteorological Administration (KMA) and daily sea surface temperature (SST) data from National Fisheries Research and Development Institute (NFRDI) (1995-2004), mean heat fluxes were estimated at the port of Yeosu. Net heat flux was transported from the air to the sea surface during February to September, and it amounts to $205 Wm^{-2}$ in daily average value in May. During October to January, the transfer of net heat flux was conversed from the sea surface to the air with $-70 Wm^{-2}$ in minimum of daily average value in December. Short wave radiation was ranged from $167 Wm^{-2}$ in December to $300 Wm^{-2}$ in April. Long wave radiation (Sensible heat) was ranged from $27 (-14) Wm^{-2}$ in July to $90 (79) Wm^{-2}$ in December. Latent heat showed $42 Wm^{-2}$ with its minimum in July and $104 Wm^{-2}$ with its maximum in October in daily average value.

• Magazine of the Korean Society of Agricultural Engineers
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• 제40권1호
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• pp.68-77
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• 1998

The purpose of this paper is to establish a method estimating the daily urban water demand using statistical analysis that is used for developing the efficient management and operation of the water supply facilities, and accurary of the model is verified by error rate and F-value. The data used in this study were the daily urban water use, the weather conditions such as temperature, precipitation, relative humidity, etc, and the day of The week. The case study was taken placed for the city of Namwon in Korea. The raw data used in this study were rearranged either by month or by season for analysis purpose, and the statistical analysis was applied to the data to obtain the regression model As a result of this study, the linear regression model was developed to estimate the daily urban water use with weather condition. The regression constant and coefficients of the model were determined for each month of a year. The accuracy of the model was within 3% of average error and within 11% of maximum error. The resulting model was found to he useful to the practical operation and management of the water supply facilities.

• Journal of Korean Society of Industrial and Systems Engineering
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• 제39권1호
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• pp.25-30
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• 2016

It is critical to forecast the maximum daily and monthly demand for power with as little error as possible for our industry and national economy. In general, long-term forecasting of power demand has been studied from both the consumer's perspective and an econometrics model in the form of a generalized linear model with predictors. Time series techniques are used for short-term forecasting with no predictors as predictors must be predicted prior to forecasting response variables and containing estimation errors during this process is inevitable. In previous researches, seasonal exponential smoothing method, SARMA (Seasonal Auto Regressive Moving Average) with consideration to weekly pattern Neuron-Fuzzy model, SVR (Support Vector Regression) model with predictors explored through machine learning, and K-means clustering technique in the various approaches have been applied to short-term power supply forecasting. In this paper, SARMA and intervention model are fitted to forecast the maximum power load daily, weekly, and monthly by using the empirical data from 2011 through 2013. $ARMA(2,\;1,\;2)(1,\;1,\;1)_7$ and $ARMA(0,\;1,\;1)(1,\;1,\;0)_{12}$ are fitted respectively to the daily and monthly power demand, but the weekly power demand is not fitted by AREA because of unit root series. In our fitted intervention model, the factors of long holidays, summer and winter are significant in the form of indicator function. The SARMA with MAPE (Mean Absolute Percentage Error) of 2.45% and intervention model with MAPE of 2.44% are more efficient than the present seasonal exponential smoothing with MAPE of about 4%. Although the dynamic repression model with the predictors of humidity, temperature, and seasonal dummies was applied to foretaste the daily power demand, it lead to a high MAPE of 3.5% even though it has estimation error of predictors.

• Magazine of the Korean Society of Agricultural Engineers
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• 제19권3호
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• pp.4483-4491
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• 1977

The results obtained are as follows; 1. The variation of the temperature in a vinyl house without heating system is similar to that of air temperature in a day. The difference of maximum temperature and minimum one in a day is 27$^{\circ}C$ which is two times greater than the daily difference of air temperature. 2. When the length of the duct is increased, the high temperature zone is built up in the direction of warm air discharge from the duct, and the low temperature zone is built up in the opposite direction of warm air discharge. But, in case of the duct length is short (0.05 L), the temperature distrubution in a vinyl house become uniform. It is concluded that the shorter length of the duct, the better the distribution of the temperature in a vinyl house is. 3. When the duct is installed at high position, the high temperature zone is built up in the upper zone of the vinyl house and the low temperature zone is built up in the lower zone. And when the position of the duct is low, the rate of temperature variation along the vertical direction become high, and the direct contact of warm air with the plant in the house is occured. It is concluded that the duct should be installed at the position of slightly higher than the plant height. 4. When the fuel consumption rate is fixed at the 101/hr, the lowest temperature warming rate in the vinyl house is 5$^{\circ}C$ without regard to the air temperature.

• Journal of People, Plants, and Environment
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• 제24권5호
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• pp.519-527
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• 2021

Background and objective: This study was conducted to establish a Plant Hardiness Zone (PHZ) map, investigate the effect of global warming on changes in PHZ, and elucidate the difference in the distribution of evergreen trees between the central and southern region within hardiness Zone 7b in Korea. Methods: Mean annual extreme minimum temperature (EMT) and related temperature fluctuation data for 40 years (1981 to 2020) in each of the meteorological observation points were extracted from the Open MET Data Portal of the Korea Meteorological Administration. Using EMT data from 60 meteorological observation points, PHZs were classified according to temperature range in the USDA Plant Hardiness Zone Map. Changes in PHZs for each decade related to the effects of global warming were analyzed. Temperature fluctuation before and after the day of EMT were analyzed for 4 areas of Seoul, Suwon, Suncheon, and Jinju falling under Zone 7b. For statistical analysis, descriptive statistics and ANOVA were performed using the IBM SPSS 22 Statistics software package. Results: Plant hardiness zones in Korea ranged from 6a to 9b. Over four decades, changes to warmer PHZ occurred in 10 areas, especially in colder ones. Based on the analysis of daily temperature fluctuation, the duration of sub-zero temperatures was at least 2 days in Seoul and Suwon, while daily maximum temperatures were above zero in Suncheon and Jinju before and after EMT day. Conclusion: It was found that the duration of sub-zero temperatures in a given area is an important factor affecting the distribution of evergreen trees in PHZ 7b.

• The Korean Journal of Applied Statistics
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• 제32권1호
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• pp.161-171
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• 2019

Varied methods have been researched continuously because the past as the daily maximum electricity demand expectation has been a crucial task in the nation's electrical supply and demand. Forecasting the daily peak electricity demand accurately can prepare the daily operating program about the generating unit, and contribute the reduction of the consumption of the unnecessary energy source through efficient operating facilities. This method also has the advantage that can prepare anticipatively in the reserve margin reduced problem due to the power consumption superabundant by heating and air conditioning that can estimate the daily peak load. This paper researched a model that can forecast the next day's daily peak load when considering the influence of temperature and weekday, weekend, and holidays in the Seasonal ARIMA, TBATS, Seasonal Reg-ARIMA, and NNETAR model. The results of the forecasting performance test on the model of this paper for a Seasonal Reg-ARIMA model and NNETAR model that can consider the day of the week, and temperature showed better forecasting performance than a model that cannot consider these factors. The forecasting performance of the NNETAR model that utilized the artificial neural network was most outstanding.

• Journal of Environmental Science International
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• 제26권12호
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• pp.1399-1405
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• 2017

We compared the spatial distribution of several heat stress indices (the Wet-Bulb Globe Temperature(WBGT) index, Environmental Stress Index (ESI), and Modified Discomfort Index(MDI)) for the heat wave of June 6~August 26, 2016, in Daegu. We calculated the heat stress indices using data from the high density urban climate observation network in Daegu. The observation system was established in February. 2013. We used data from a total of 38 air temperature observation points (23 thermometers and 18 automatic weather stations). The values of the heat stress indices indicated that the danger level was very high from 0900-2000h in downtown Daegu. The daily maximum value of the WBGT was greater than or equal to $35^{\circ}C$. The differences in the heat stress indices from downtown and rural areas were higher in the daytime than at nighttime. The maximum difference was about 4 before and after 1400h, and the time variations of the heat stress indices corresponded well. Thus, we were able to confirm that the ESI and MDI can be substituted with the WBGT index.

• Journal of Korean Society for Atmospheric Environment
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• 제24권5호
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• pp.604-612
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• 2008

The ozone concentration is one of the important environmental issue for measurement of the atmospheric condition of the country. This study focuses on applying the Autoregressive Error (ARE) model for analyzing the ozone data at middle part of the Gyeonggi-Do, Anyang monitoring site in Korea. In the ARE model, eight meteorological variables and four pollution variables are used as the explanatory variables. The eight meteorological variables are daily maximum temperature, wind speed, amount of cloud, global radiation, relative humidity, rainfall, dew point temperature, and water vapor pressure. The four air pollution variables are sulfur dioxide $(SO_2)$, nitrogen dioxide $(NO_2)$, carbon monoxide (CO), and particulate matter 10 (PM10). The result shows that ARE models both overall and monthly data are suited for describing the oBone concentration. In the ARE model for overall ozone data, ozone concentration can be explained about 71% to by the PM10, global radiation and wind speed. Also the four types of ARE models for high level of ozone data (over 80 ppb) have been analyzed. In the best ARE model for high level of ozone data, ozone can be explained about 96% by the PM10, daliy maximum temperature, and cloud amount.

• Journal of Institute of Convergence Technology
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• 제4권2호
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• pp.27-33
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• 2014

To well design the solar energy system using solar energy, the correlation to calculate solar irradiation is basically needed. So, this study was performed to reveal the relationships between the solar irradiation and four meteorological observation data(dry bulb temperature, relative humidity, sunshine duration, and cloud cover) which are different from previous other researches. And then, we finally proposed the first order non-linear correlation from the measured solar irradiation using four meteorological observation data with MINITAB. To show the deviation of the solar irradiation between measured and calculated, this study compared using the daily total solar irradiance and the maximum peak value. From those results, the calculation error was estimated about maximum 25.4% for the daily total solar irradiance. The error of the solar irradiation between measured and calculated was made from the curve fitting error. So, solar irradiation prediction correlation with higher accuracy can be obtained using 2nd or higher order terms with four meteorological observation data.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 한국농림기상학회 2002년도 추계 학술발표논문집
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• pp.77-80
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• 2002

기온은 생물의 대사과정에 직접적인 영향을 끼침으로서 생장과 발육을 결정하는 중요한 환경요인이며, 특히 식물은 개체 및 군락 수준에서 기온의 일 변화, 계절변화, 혹은 영년 변화에 반응한다. 최근의 농업 및 삼림 생태계 연구는 기온을 비롯한 환경요인의 영향을 생리과정의 정량적 모의를 근거로 이해하고, 이를 넓은 지역으로 확대하여 다양한 시간적 주기로 예측하는 방향으로 나아가고 있다 (Chung et al., 2002).(중략)