• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.7
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• pp.1144-1150
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• 2016

Short-term load forecasting is essential to the electricity pricing and stable power system operations. The conventional weekday 24-hour load forecasting algorithms consider the temperature model to forecast maximum load and minimum load. But 24-hour load pattern forecasting models do not consider temperature effects, because hourly temperature forecasts were not present until the latest date. Recently, 3 hour temperature forecast is announced, therefore hourly temperature forecasts can be produced by mathematical techniques such as various interpolation methods. In this paper, a new 24-hour load pattern forecasting method is proposed by using similar day search considering the hourly temperature. The proposed method searches similar day input data based on the anomalous weather features such as continuous temperature drop or rise, which can enhance 24-hour load pattern forecasting performance, because it uses the past days having similar hourly temperature features as input data. In order to verify the effectiveness of the proposed method, it was applied to the case study. The case study results show high accuracy of 24-hour load pattern forecasting.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.25 no.6
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• pp.310-316
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• 2013

This study was designed to investigate a method for short-term, real-time energy demand prediction, to cope with changing loads for the effective operation and management of buildings. Through a case study, a novel methodology for real-time energy demand prediction with the use of weather forecasting data was suggested. To perform the input and output operations of weather data, and to calculate solar radiation and EnergyPlus, the BCVTB (Building Control Virtual Test Bed) was designed. Through the BCVTB, energy demand prediction for the next 24 hours was carried out, based on 4 real-time weather data and 2 solar radiation calculations. The weather parameters used in a model equation to calculate solar radiation were sourced from the weather data of the KMA (Korea Meteorological Administration). Depending on the local weather forecast data, the results showed their corresponding predicted values. Thus, this methodology was successfully applicable to anywhere that local weather forecast data is available.

• The Korean Journal of Applied Statistics
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• v.22 no.1
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• pp.221-235
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• 2009

This manuscript illustrates the comparative study between ARIMA and Exponential Smoothing modeling to develop forest fire forecasting system using different weather parameters. In this paper, authors have developed the most suitable and closest forecasting models like ARIMA and Exponential Smoothing techniques using different weather parameters. Authors have considered the extremes of the Wind speed, Radiation, Maximum Temperature and Deviation Temperature of the Summer Season form March to June month for the Ranchi Region in Jharkhand. The data is taken by own resource with the help of Automatic Weather Station. This paper consists a deep study of the effect of extreme values of the different parameters on the weather fluctuations which creates forest fires in the region. In this paper, the numerical illustration has been incorporated to support the present study. Comparative study of different suitable models also incorporated and best fitted model has been tested for these parameters.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.262-264
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• 2002

The characteristic features of Malaysia's climate is has stable temperature, with high humidity and copious rainfall. Weather forecasting is an important task in Malaysia as it could affetcs man irrespective of mans job, lifestyle and activities especially in the agriculture. In Malaysia, numerical method is the common used method to forecast weather which involves a complex of mathematical computing. The models used in forecasting are supplied by other counties such as Europe and Japan. The goal of this project is to forecast weather using another technology known as artificial neural network. This system is capable to learn the pattern of rainfall in order to produce a precise forecasting result. The supervised learning technique is used in the loaming process.

• International Journal of Industrial Entomology and Biomaterials
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• v.25 no.2
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• pp.163-169
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• 2012

A decrease in pass ratio of Korean exporting pears causes several negative effects including an increase in pesticide dependency. In this study, we attempted to establish the pest forecasting management system, composed of weekly field forecasting by pear farmers, meteorological data obtained by automatic weather station (AWS), newly designed internet web page ($\underline{http://pearpest.jnu.ac.kr/}$) as information collecting and providing ground, and information providing service. The weekly field forecasting information on major pear diseases and pests was collected from the forecasting team composed of five team leaders from each pear exporting complex. Further, an abridged weather information for the prediction of an infestation of major disease (pear scab) and pest (pear psylla and scale species) was obtained from an AWS installed at Bonghwang in Naju City. Such information was then promptly uploaded on the web page and also publicized to the pear famers specializing in export. We hope this pest forecasting management system increases the pass ratio of Korean exporting pears throughout establishment of famer-oriented forecasting, inspiring famers' effort for the prevention and forecasting of diseases and pests occurring at pear orchards.

• Journal of the Korean Geographical Society
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• v.31 no.3
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• pp.469-488
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• 1996

The defining of weather forecasting regions is possible. since the representativeness of regional weather can by reasonably clarified in terms of weather entropy and the use of information ratio. In this paper, the weather entropy and information ratio were derived numerially from using the information theory. The typical weather characteristics were clarified and defined in the homogeneous weather forecasting regions of the southern parts of Korea. The data used for this study are the daily precipitation and cloudiness during the recent five years (1990-1994) at 42 stations in southern parts of Korea. It is divided into four classes of fine, clear, cloudy and rainy. The results are summarized as follows: 1. The maximum value of weather entropy in study area is 2.009 vits in Yosu in July, and the minimum one is 1.624 bits in Kohung in October. The mean value of weather entropy is maximal in July, on the other hand, minimal in October during four season. The less the value of entropy is, the stabler the weather is. While the bigger the value of entropy is, the more changeable the weather is. 2. The deviation from mean value of weather entropy in southern parts of Korea, with the positive and the negative parts, shows remarkably the distributional tendency of the east (positive) and the west (negative) in January but of the south (positive) and the north (negative) in July. It also clearly shows the distributional tendency of the east (postive) and the west(negative) in the coastal region in April, and of X-type (southern west and northern east: negative) in Chiri Mt. in October. 3. In southern parts, the average information ratio maximaly appear 0.618 in Taegu area in July, whereas minimally 0.550 in Kwangju in October. Particularly the average information ratio of Pusan area is the greatest in April, but the smallest in October. And in Taegu, Kwangju, and Kunsan, it is the greatest in April, January, and July, but the smallest in Jyly, July, and pril. 4.The narrowest appreance of weather representativeness is in July when the Kwangju is the center of the weather forecasting. But the broadest one is in April when Taegu is the center of weather forecasting. 5. The defining of weather forecasting regions in terms of the difference of information ratio most broadly shows up in July in Pusan including the whole Honam area and the southern parts of Youngnam when the Pusan-Taegu is the basis of the application of information ratio. Meanwhile, it appears most broadly in January in Taegu including the whole southern parts except southern coastal area.

• International Journal of Computer Science & Network Security
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• v.24 no.3
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• pp.196-200
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• 2024

Weather forecasting is considered to be of utmost important among various important sectors such as flood management and hydro-electricity generation. Although there are various numerical methods for weather forecasting but majority of them are reported to be Mechanistic computationally demanding due to their complexities. Therefore, it is necessary to develop and build models for accurately predicting the weather conditions which are faster as well as efficient in comparison to the prevalent meteorological models. The study has been undertaken to forecast various atmospheric parameters in the city of Bangalore using Naïve Bayes algorithms. The individual parameters analyzed in the study consisted of wind speed (WS), wind direction (WD), relative humidity (RH), solar radiation (SR), black carbon (BC), radiative forcing (RF), air temperature (AT), bar pressure (BP), PM₁₀ and PM_2.5 of the Bangalore city collected from Air Quality Monitoring Station for a period of 5 years from January 2015 to May 2019. The study concluded that Naive Bayes is an easy and efficient classifier that is centered on Bayes theorem, is quite efficient in forecasting the various air pollution parameters of the city of Bangalore.

• Journal of the Korean Data and Information Science Society
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• v.27 no.6
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• pp.1601-1607
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• 2016

Recently, weather information has been increasingly used in various area. This study presents the necessity of hourly weather information for electricity demand forecasting through correlation analysis and multivariate regression model. Hourly weather data were collected by Meteorological Administration. Using electricity demand data, we considered TBATS exponential smoothing model with a sliding window method in order to forecast electricity demand. In this paper, we have shown that the incorporation of weather infromation into electrocity demand models can significantly enhance a forecasting capability.

• Korean Journal of Agricultural and Forest Meteorology
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• v.10 no.3
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• pp.107-112
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• 2008

"Jeung-Bo-San-Lim-Gyeong-Je" (meaning "Revised Forest Management") has been well recognized as the informative document that introduces scientific knowledge and experiences of Korean ancestors regarding weather and climate. The tradition of Gwan-Cheon-Mang-Gi(i.e., empirical forecasting of short-term weather phenomena based on the status of cloud or sky) has been continuously utilized as a civilian weather forecasting method and even for very short-term weather prediction by operational forecasters these days. This agricultural technology textbook, published during the Great King Youngjo in Chosun-Dynasty, may be regarded as a poorly written document from the modern standpoint. Nonetheless, this study demonstrates that by and large the empirical knowledge contained in the book is indeed science based although their applications are limited to several hours for local forecasts in agricultural practices and daily living. For example, the wisdom of keeping water at an optimum level in a paddy field after sowing to prevent young seedlings from late frost damages was not at all different from the present technique of vinyl covered seedling nursery.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.136-136
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• 2021

In this study, a deep convolutional neural network (DCNN) model is proposed for short-term precipitation forecasting using weather radar-based images. The DCNN model is a combination of convolutional neural networks, autoencoder neural networks, and U-net architecture. The weather radar-based image data used here are retrieved from competition for rainfall forecasting in Korea (AI Contest for Rainfall Prediction of Hydroelectric Dam Using Public Data), organized by Dacon under the sponsorship of the Korean Water Resources Association in October 2020. This data is collected from rainy events during the rainy season (April - October) from 2010 to 2017. These images have undergone a preprocessing step to convert from weather radar data to grayscale image data before they are exploited for the competition. Accordingly, each of these gray images covers a spatial dimension of 120×120 pixels and has a corresponding temporal resolution of 10 minutes. Here, each pixel corresponds to a grid of size 4km×4km. The DCNN model is designed in this study to provide 10-minute predictive images in advance. Then, precipitation information can be obtained from these forecast images through empirical conversion formulas. Model performance is assessed by comparing the Score index, which is defined based on the ratio of MAE (mean absolute error) to CSI (critical success index) values. The competition results have demonstrated the impressive performance of the DCNN model, where the Score value is 0.530 compared to the best value from the competition of 0.500, ranking 16^th out of 463 participating teams. This study's findings exhibit the potential of applying the DCNN model to short-term rainfall prediction using weather radar-based images. As a result, this model can be applied to other areas with different spatiotemporal resolutions.