• 정보통신설비학회논문지
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• 제9권3호
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• pp.107-111
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• 2010

This paper is concerned with data center remote management service for demanding forecasting and reduction of energy usage. More particularly, intelligent server rack, mounted on inside of the data center, collects information about energy usage and temperature per server. Using this information, management platform forecasts energy demand in the future and automatically makes report according green environment raw. By providing the remote management service through remote terminals, users are not tied to a time and place to control device inside the data center. In this way, the data center remote management service enhances operability of the facility.

• 한국군사과학기술학회지
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• 제27권3호
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• pp.406-415
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• 2024

As the equipment of the military has become more advanced and expensive, the cost of securing spare parts is also constantly increasing along with the increase in equipment assets. In particular, forecasting demand for spare parts one of the important management tasks in the military, and the accuracy of these predictions is directly related to military operations and cost management. However, because the demand for spare parts is intermittent and irregular, it is often difficult to make accurate predictions using traditional statistical methods or a single statistical or machine learning model. In this paper, we propose a model that can increase the accuracy of demand forecasting for irregular patterns of spare parts demanding by using a combination of statistical and machine learning algorithm, and through experiments on Cheonma spare parts demanding data.

• 한국경영과학회:학술대회논문집
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• 한국경영과학회 2007년도 추계학술대회 및 정기총회
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• pp.477-481
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• 2007

The purpose of this study is to provide the ideal forecasting model of cadastral survey work load through the Economeatric Analysis of Time Series, Granger Causality and VAR Model Analysis, it suggested the forecasting reference materials for the total amount of cadastral survey general work load. The main result is that the derive of the environment variables which affect cadastral survey general work load and the outcome of VAR(vector auto regression) analysis materials(impulse response function and forecast error variance decomposition analysis materials), which explain the change of general work load depending on altering the environment variables. And also, For confirming the stability of time series data, we took a unit root test, ADF(Augmented Dickey-Fuller) analysis and the time series model analysis derives the best cadastral forecasting model regarding on general cadastral survey work load. And also, it showed up the various standards that are applied the statistical method of econometric analysis so it enhanced the prior aggregate system of cadastral survey work load forecasting.

• 콘크리트학회지
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• 제18권1호
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• pp.44-51
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• 2006

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 C
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• pp.929-931
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• 1998

This paper describes the peak load forecasting technique of distribution transformers with correlation equation. While customers are demanding safe energy supply, conventional correlation equation that is used for load management of distribution transformers in domestic has some problems. To get accurate correlation equation, se-correlation equation were examined using new collected using the measuring instrument dev for this study. It was recognized that the qua equation was the most accurate for peak forecasting from working electrical energy.

• International Journal of Computer Science & Network Security
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• 제24권3호
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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.

• 인터넷정보학회논문지
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• 제18권1호
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• pp.121-129
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• 2017

수리부속 수요예측은 장비가동률 향상과 국방 운영 예산 효율화 제고를 위한 국방 군수 분야의 핵심 과제 중 하나이다. 현재 우리군은 수리부속 소요 데이터를 활용한 시계열 기법으로 과거 데이터 분석을 통해 수리부속 수요예측에 활용하고 있으나 정확도 제고에 지속적인 노력이 요구되고 있는 실정이다. 이에 본 연구에서는 지난 5개년의 수리부속 18,476개 품목의 수요데이터를 수집하고 데이터마이닝 기법을 활용한 수리부속 수요예측 모델을 제안하였다. 제안한 모델에 따른 실험 결과는 기존 시계열 기법에 비해 개선된 수요예측 정확도를 보였다.

• 통상정보연구
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• 제16권3호
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• pp.73-96
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• 2014

본 연구는 우리나라 수출 상위 5개 품목 중 하나인 자동차 수출을 대상으로, 승용차 브랜드별 단기 수출수요에 영향을 미치는 이론적 잠재요인을 발굴 및 설계하여 이론적 수출수요예측모델을 개발하고, 다변량시계열분석 기반의 VAR(Vector Auto Regressive)모형을 이용한 실증분석을 통해 개별상품과 시장특성이 반영된 단기수출수요예측모델을 검정하고자 하였다. 따라서 미국에 수출되고 있는 우리나라 소형 승용차 2개 브랜드(엑센트, 아반떼)에 대해 VAR모형을 이용한 분기단위 단기수요예측모델을 개발하고, 브랜드별 예측모델을 통해 산출된 t+1분기 시점의 예측값과 실제 판매된 판매대수를 대상기간을 1분기씩 달리하여 비교평가 하였다. 그 결과 엑센트와 아반떼의 RMSE %는 각각 4.3%와 20.0%로 났으며, 일평균 판매량을 기준으로 보았을 때 엑센트는 3.9일에 해당하고 아반떼는 18.4일에 해당하는 물량임을 알 수 있었다. 따라서 본 연구의 단기수출수요예측모델은 예측력과 검정시점별 일관성 측면에서 활용성이 높은 것으로 평가할 수 있었다.

• 한국조명전기설비학회:학술대회논문집
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• 한국조명전기설비학회 1996년도 추계학술발표회논문집
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• pp.101-103
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• 1996

This Paper describes Peak load forecasting technique of pole transformers with correlation equation. While customers are demanding safe energy supply, current correlation equation that is used for load management of pole transformers has some problems. To get accurate correlation equation. several correlation equation were examined using past data and nu data collected using the measuring instrument developed for this study. It was recognized that the quadratic equation was the most accurate for peak load forecasting from working electrical energy.

• 응용통계연구
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• 제32권1호
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• pp.69-81
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• 2019

본 논문에서는 딥러닝을 이용한 이변량 장기종속시계열(long-range dependent time series) 예측을 고려하였다. 시계열 데이터 예측에 적합한 LSTM(long short-term memory) 네트워크를 이용하여 이변량 장기종속시계열을 예측하고 이를 이변량 FARIMA(fractional ARIMA) 모형인 FIVARMA 모형과 VARFIMA 모형과의 예측 성능을 실증 자료 분석을 통해 비교하였다. 실증 자료로는 기능적 자기공명 영상(fMRI) 및 일일 실현 변동성(daily realized volatility) 자료를 이용하였으며 표본외 예측(out-of sample forecasting) 오차 비교를 통해 예측 성능을 측정하였다. 그 결과, FIVARMA 모형과 VARFIMA 모형의 예측값에는 미묘한 차이가 존재하며, LSTM 네트워크의 경우 초매개변수 선택으로 복잡해 보이지만 계산적으로 더 안정되면서 예측 성능도 모수적 장기종속시계열과 뒤지지 않은 좋은 예측 성능을 보였다.