• Environmental Engineering Research
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• v.22 no.2
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• pp.162-168
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• 2017

Rainfall is one of the most important phenomena of the natural system. In Bangladesh, agriculture largely depends on the intensity and variability of rainfall. Therefore, an early indication of possible rainfall can help to solve several problems related to agriculture, climate change and natural hazards like flood and drought. Rainfall forecasting could play a significant role in the planning and management of water resource systems also. In this study, univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) model was used to forecast monthly rainfall for twelve months lead-time for thirty rainfall stations of Bangladesh. The best SARIMA model was chosen based on the RMSE and normalized BIC criteria. A validation check for each station was performed on residual series. Residuals were found white noise at almost all stations. Besides, lack of fit test and normalized BIC confirms all the models were fitted satisfactorily. The predicted results from the selected models were compared with the observed data to determine prediction precision. We found that selected models predicted monthly rainfall with a reasonable accuracy. Therefore, year-long rainfall can be forecasted using these models.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1874-1885
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• 2018

The customer side operation is getting more complex in a smart grid environment because of the adoption of renewable resources. In performing energy management planning or scheduling, it is essential to forecast non-controllable resources accurately and robustly. The PV system is one of the common renewable energy resources in customer side. Its output depends on weather and physical characteristics of the PV system. Thus, weather information is essential to predict the amount of PV system output. However, weather forecast usually does not include enough solar irradiation information. In this study, a PV system power output prediction model (PPM) under limited weather information is proposed. In the proposed model, meteorological radiation model (MRM) is used to improve cloud cover radiation model (CRM) to consider the seasonal effect of the target region. The results of the proposed model are compared to the result of the conventional CRM prediction method on the PV generation obtained from a field test site. With the PPM, root mean square error (RMSE), and mean absolute error (MAE) are improved by 23.43% and 33.76%, respectively, compared to CRM for all days; while in clear days, they are improved by 53.36% and 62.90%, respectively.

• Atmosphere
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• v.30 no.4
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• pp.405-420
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• 2020

In this study, we investigate the predictability of intraseasonal monthly temperature and precipitation variations using hindcast datasets from eight global circulation models participating in the operational multi-model ensemble (MME) seasonal prediction system of the Asia-Pacific Economic Cooperation Climate Center for the 1983~2010 period. These intraseasonal monthly variations are defined by categorical deterministic analysis. The monthly temperature and precipitation are categorized into above normal (AN), near normal (NN), and below normal (BN) based on the σ-value ± 0.43 after standardization. The nine patterns of intraseasonal monthly variation are defined by considering the changing pattern of the monthly categories for the three consecutive months. A deterministic and a probabilistic analysis are used to define intraseasonal monthly variation for the multi-model consisting of numerous ensemble members. The results show that a pattern (pattern 7), which has the same monthly categories in three consecutive months, is the most frequently occurring pattern in observation regardless of the seasons and variables. Meanwhile, the patterns (e.g., patterns 8 and 9) that have consistently increasing or decreasing trends in three consecutive months, such as BN-NN-AN or AN-NN-BN, occur rarely in observation. The MME and eight individual models generally capture pattern 7 well but rarely capture patterns 8 and 9.

• Journal of Korea Society of Industrial Information Systems
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• v.15 no.5
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• pp.63-69
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• 2010

Forecasting game usages hours can supply good information resolving intensive server access and ensuring stable game service. In this paper, we applied various time series analysis methods to forecast game usage hours in 2009 on famous "Ion" and "Sudden Attack" games. According to the experiment, the seasonal variation method showed better performance forecasting actual usage hours.

• Korea Trade Review
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• v.45 no.5
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• pp.75-89
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• 2020

The purpose of this paper was to analyze the trend of container volume using the Seasonal Autoregressive Intergrated Moving Average (SARIMA) model. To this end, this paper used monthly time-series data of the East Sea Rim from 2001 to 2019. As a result, the SARIMA(2,1,1)12 model was identified as the most suitable model, and the superiority of the SARIMA model was demonstrated by comparative analysis with the ARIMA model. In addition, to confirmed forecasting accuracy of SARIMA model, this paper compares the volume of predict container to the actual volume. According to the forecast for 24 months from 2020 to 2021, the volume of containaer increased from 60,100,000Ton in 2020 to 64,900,000Ton in 2021

• The Korean Journal of Applied Statistics
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• v.12 no.2
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• pp.491-503
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• 1999

시계열자료에는 흔히 대부분의 자료에서 벗어나는 이상치들이 포함되어 있는데, 이러한 자료는 관측치들 사이의 종속구조로 인해 분석과정에 영향을 끼칠 수 있고, 특히 연속시점에서 발생하는 경우에 그 영향이 매우 심각할 수 있다. 본 논문에서는 연속이상치(PO)가 Winters 계절지수모형의 분석과정에 미치는 영향을 유도하고, 예측 평균제곱오차(MSFE)를 구하여 연속이상치가 예측에 미치는 영향을 제시하였다. 또한, 실제자료를 이용하여 연속이상치의 영향을 실증적으로 분석하였다.

• Proceedings of the Korea Concrete Institute Conference
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• 1991.10a
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• pp.146-151
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• 1991

In recent years a reasonable supply and demand plan of construction materials which is based upon an accurate forecast has been greatly required to prevent construction works from delaying and slapdash. To meet an above requirement, a short-term forecasting system of construction materials, in this paper, is established, which is approached in engineering aspect and emerged from conventional forecasting systems. The major considerations in setting up this system are the distributed lag of consrection business indicators and seasonal variations in consumption of constuction materials.

• Proceedings of The Korean Society of Agricultural and Forest Meteorology Conference
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• 2019.08a
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• pp.289-289
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• 2019

• Journal of Korea Water Resources Association
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• v.44 no.5
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• pp.339-350
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• 2011

This study developed a climate informed Bayesian nonstationary frequency model which allows us to forecast seasonal summer rainfall at Nakdong River. We constructed a 37-year summer rainfall data set from 10 weather stations within Nakdong river basin, and two climate indices from sea surface temperature (SST) and outgoing longwave radiation (OLR) were derived through correlation analysis. The selected SST and OLR have been widely acknowledged as a climate driver for summer rainfall. The developed model was applied first to the 2010-year summer rainfall (888.1 mm) in order to assure ourself. We demonstrated model performance by comparing posterior distributions. It was confirmed that the proposed model is able to produce a reasonable forecast. The forecasted value is about 858.2 mm, and the difference between forecast and observation is about 30 mm. As the second case study, 2011-year summer rainfall forecast was made using an observed winter SSTs and an assumed 50% value of OLRs. The forecasted value is 967.7 mm and associated exceedance probability over average summer rainfall 680 mm is 92.9%. In addition, 50-year return period for summer rainfall was projected through the nonstationary frequency model. An exceedance probability over 1,400 mm corresponding to the 50-year return level is about 73.7%.

• Journal of Korea Port Economic Association
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• v.31 no.1
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• pp.69-84
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• 2015

The purpose of this study is to forecast the seaborne trade volume during January 1994 to December 2014 using the multiplicative seasonal autoregressive integrated moving average (ARIMA) along with intervention factors and an artificial neural network (ANN) model. Diagnostic checks of the ARIMA model were conducted using the Ljung-Box Q and Jarque-Bera statistics. All types of ARIMA process satisfied the basic assumption of residuals. The ARIMA(2,1,0) $(1,0,1)_{12}$ model showed the lowest forecast error. In addition, the prediction error of the artificial neural network indicated a level of 5.9% on hidden layer 5, which suggests a relatively accurate forecasts. Furthermore, the ex-ante predicted values based on the ARIMA model and ANN model are presented. The result shows that the seaborne trade volume increases very slowly.