• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.17 no.2
• /
• pp.199-204
• /
• 2017

In this study, we present a home appliance load identification algorithm. The algorithm utilizes complex sensory data in order to improve the existing NIALM using total power usage information. We define the influence graph between the appliance status and the measured sensor data. The device identification prediction result is calculated as the weighted sum of the predicted value of the sensor data processing algorithm and the predicted value based on the total power usage. We evaluate proposed algorithm to compare appliance identification accuracy with the existing NIALM algorithm.

• The Journal of the Convergence on Culture Technology
• /
• v.10 no.4
• /
• pp.693-700
• /
• 2024

As of the end of March 2022, the total area of domestic industrial complexes is 606 km2, which is only about 0.6% of the total land area. However, as of 2018, the annual energy consumption of domestic industrial complexes is 110,866.1 thousand TOE, accounting for 53.5% of the country's total energy consumption and 83.1% of the entire industrial sector energy consumption. In addition, industrial complexes have a significant impact on the environment, accounting for 45.1% of the country's total greenhouse gas emissions and 76.8% of industrial sector greenhouse gas emissions. Under this background, in this study, in order to contribute to the energy efficiency of industrial complexes, a prediction study on energy demand and supply for an industrial complex in Korea using machine learning was conducted. In addition, a simulator UI screen was designed to more efficiently convey information on energy demand/supply prediction results and energy consumption status. Among the machine learning algorithms, Multi-Layer Perceptron (MLP) was used, and Bayesian Optimization was applied as an optimization technique for the prediction model. The energy prediction model for the industrial complex built in this study showed a prediction accuracy of 87.90% for compressed air demand and 99.54% for the flow rate available for the public air compressor.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.16 no.12
• /
• pp.8576-8584
• /
• 2015

Load forecasting is needed to make supply and demand plan for a stable supply of electricity. It is also necessary for optimal operational plan of the power system planning. In particular, in order to ensure stable power supply, long-term load forecasting is important. And regional load forecasting is important for tightening supply stability. Regional load forecasting is known to be an essential process for the optimal state composition and maintenance of the electric power system network including transmission lines and substations to meet the load required for the area. Therefore, in this paper we propose a forecasting method using SARIMA during the 12 months (long-term/mid-term) load forecasting by 16 regions of the South Korea.

• Journal of IKEEE
• /
• v.23 no.4
• /
• pp.1373-1380
• /
• 2019

In order to increase energy efficiency, developments of the advanced metering infrastructure (AMI) in the smart grid technology have recently been actively conducted. An essential part of AMI is analyzing power consumption and forecasting consumption patterns. In this paper, we analyze the power consumption and summarized the data errors. Monthly power consumption patterns are also analyzed using the k-means clustering algorithm. Forecasting the consumption pattern by each household is difficult. Therefore, we first classify the data into 100 clusters and then predict the average of the next day as the daily average of the clusters based on the deep neural network. Using practically collected AMI data, we analyzed the data errors and could successfully conducted power forecasting based on a clustering technique.

• The Korean Journal of Applied Statistics
• /
• v.24 no.4
• /
• pp.567-573
• /
• 2011

This study develops a predictive model for household water end-uses based on data that have measured household characteristics, housing characteristics and other items, surveyed over 3 years in Korea. However, the measured data was left-skewed and it was not fitted to normal distribution. The parameter estimate were biased when using a multiple regression model. In addition, the results of the testing for the model were usually of significance due to the tiny residual from a large number of observations. In order to solve the problem, we suggested log-normal regression model and Weibull regression model as alternatives. The results of this study can be utilized in the planning stages of water and waste water facilities.

• Journal of the Korea Society of Computer and Information
• /
• v.29 no.7
• /
• pp.1-9
• /
• 2024

In this paper, we propose BRAIDS, a boot storm mitigation plan consisting of an AI-based VDI usage prediction system and a virtual machine boot scheduler system, to alleviate boot storms and improve service stability. Virtual Desktop Infrastructure (VDI) is an important technology for improving an organization's work productivity and increasing IT infrastructure efficiency. Boot storms that occur when multiple virtual desktops boot simultaneously cause poor performance and increased latency. Using the xgboost algorithm, existing VDI usage data is used to predict future VDI usage. In addition, it receives the predicted usage as input, defines a boot storm considering the hardware specifications of the VDI server and virtual machine, and provides a schedule to sequentially boot virtual machines to alleviate boot storms. Through the case study, the VDI usage prediction model showed high prediction accuracy and performance improvement, and it was confirmed that the boot storm phenomenon in the virtual desktop environment can be alleviated and IT infrastructure can be utilized efficiently through the virtual machine boot scheduler.

• Journal of Integrative Natural Science
• /
• v.15 no.2
• /
• pp.73-81
• /
• 2022

본 연구는 다중이용시설 이용자들의 쾌적함과 안전 그리고 시설내부 에너지 사용량의 최적 절감을 위하여 이용자수를 분석예측한 정보에 따른 공기질품질제어시스템 운영을 통해 국민 중심의 안전하고 쾌적한 서비스를 제공할 필요로 수행되었다. 이를 위하여 실내유동인구수를 카운팅하는 로컬시스템을 제작하고 수집된 유동인구 카운팅 정보를 시계열 모델링을 기반으로 분석예측하는 연구를 진행하였다. 개발된 시스템 성능평가 결과 유동인구 카운팅시스템은 95% 이상 정확도를 보여주었고, 예측시스템은 83~95% 정확도를 확보하였다. 본 연구결과 개발된 시스템은 다중이용시설에 즉시 적용가능하며 향후 남녀노소 인식을 진행하고 이를 예측한 정보에 의한 보다 다양한 서비스 개발을 추진할 계획이다.

• Journal of Korea Water Resources Association
• /
• v.33 no.S1
• /
• pp.788-793
• /
• 2000

본 연구는 k서로 특성이 다른 8개의 도시를 검토 대상지역으로 선정하여 사용수량의 추정방법중 이론 곡선법을 이용하는 경우의 포화값 K의 영향을 비교 검토한 것이다. 이 연구결과는 상수사용량을 예측할 때 일어날 수 있는 오류를 최소한으로 줄이고 해당도시의 예측값을 결정할 때 보다 합리적으로 접근하는데 기초자료가 될 수 있을 것이다.

• Proceedings of the Korean Information Science Society Conference
• /
• 2003.10a
• /
• pp.337-339
• /
• 2003

본 논문은 내장형 시스템의 전력 감소를 위해 사용되는 과거 사용량 기반의 DVS의 단점 인 응용 프로그램의 수행 성능 저하를 보상하기 위해, 운영체제의 스케줄러에서 제공하는 태스크의 미래정보를 이용하는 기법을 제안한다. 대표적 내장형 운영체제인 WinCE.net에서의 스케줄러는 제한된 자원의 효율적 관리를 위하여 동일 응용프로그램의 태스크들을 관리하면서 다음 태스크 시행시간 정보를 갖게 된다. 이러한 룩 어헤드(look ahead)정보와 과거사용량기법을 혼합한 혼합예측기법이 실제 내장형 시스템에서 전력소비를 감소시키며 응용프로그램의 수행 성능보상을 할 수 있음을 보인다.

• Journal of Korea Society of Industrial Information Systems
• /
• v.10 no.1
• /
• pp.9-15
• /
• 2005

Congestion control is an important research area in computer network. In this paper, I provided strategy of congestion pricing with differentiated services. And, suggested forecasting model of access that considered differentiated pricing, delay time, satisfaction using logistic regression. In a forecasting model of access with logistic regression technique, it is shown that coefficient of determination using suggested model is $70.7\%$.