• Proceedings of the Korea Information Processing Society Conference
• /
• 2019.10a
• /
• pp.817-819
• /
• 2019

다양한 학습 모델이 발전하고 있는 지금, 학습을 통한 다양한 시도가 진행되고 있다. 이중 에너지 분야에서 많은 연구가 진행 중에 있으며, 대표적으로 BEMS(Building energy Management System)를 볼 수 있다. BEMS의 경우 건물을 기준으로 건물에서 생성되는 다양한 DATA를 이용하여, 에너지 예측 및 제어하는 다양한 기술이 발전해가고 있다. 하지만 FEMS(Factory Energy Management System)에 관련된 연구는 많이 발전하지 못했으며, 이는 BEMS와 FEAMS의 차이에서 비롯된다. 본 연구에서는 실제 공장에서 수집한 DATA를 기반으로 하여, 전력량 예측을 하였으며 예측을 위한 기술로 시계열 DATA 분석 방법인 LSTM 알고리즘을 이용하여 진행하였다.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.28 no.5B
• /
• pp.603-614
• /
• 2008

End-uses of household water have been changed by a life style, housing type, weather, water rate and water supply facilities etc. and those variables can be considered as an internal and exogenous factors to estimate long-term demand forecasts. Analysis of influential factors on water consumption in households would give an explanation to cause on the change of trend and would help predicting the water demand of end-use in household. The purpose of this study is to analyze the demand trends and patterns of household water uses by metering and questionnaire such as occupation, revenue, numbers of family member, housing types, age, floor area and installation of water saving device, etc. The peak water uses were shown at Saturday among weekdays and July in a year based on the analysis results of water use pattern. A steep increase of total water volume can be found in the analysis of water demand trend according to temperature from $-14^{\circ}C$ to $0^{\circ}C$, while there are no significant variations in the phase of more than $0^{\circ}C$, with an almost stable demand. Washbowl water shows the highest and toilet water shows the lowest relation with temperature in correlation analysis results. In the results of ANOVA to find the significant difference in each unit water use by exogenous factors such as housing type, occupation, number of generation, residential area and income et al., difference was shown in bathtub water by housing type and shown in kitchen, toilet and miscellaneous water by numbers of resident. Especially, definite differences in components except washbowl and bathtub water, could be found by numbers of resident. Based on the result, average residents in a house should be carefully considered and the results can be applied as reference information, in decision making process for predicting water demand and establishing water conservation policy. It is expected that these can be used as design factors in planning stage for water and wastewater facilities.

• The Korean Journal of Applied Statistics
• /
• v.32 no.1
• /
• pp.149-160
• /
• 2019

The electricity consumption time series data of 'A' University from July 2016 to June 2017 is analyzed via nonparametric functional data clustering since the time series data can be regarded as realization of continuous functions with dependency structure. We use a Bouveyron and Jacques (Advances in Data Analysis and Classification, 5, 4, 281-300, 2011) method based on model-based functional clustering with an FEM algorithm that assumes a Gaussian distribution on functional principal components. Clusterwise analysis is provided with cluster mean functions, densities and cluster profiles.

• Journal of Korea Water Resources Association
• /
• v.54 no.spc1
• /
• pp.1083-1093
• /
• 2021

It is an essential to predict water usage for establishing an optimal supply operation plan and reducing power consumption. However, the water usage by consumer has a non-linear characteristics due to various factors such as user type, usage pattern, and weather condition. Therefore, in order to predict the water consumption, we proposed the methodology linking various techniques that can consider non-linear characteristics of water use and we called it as KWD framework. Say, K-means (K) cluster analysis was performed to classify similar patterns according to usage of each individual consumer; then Wavelet (W) transform was applied to derive main periodic pattern of the usage by removing noise components; also, Deep (D) learning algorithm was used for trying to do learning of non-linear characteristics of water usage. The performance of a proposed framework or model was analyzed by comparing with the ARMA model, which is a linear time series model. As a result, the proposed model showed the correlation of 92% and ARMA model showed about 39%. Therefore, we had known that the performance of the proposed model was better than a linear time series model and KWD framework could be used for other nonlinear time series which has similar pattern with water usage. Therefore, if the KWD framework is used, it will be possible to accurately predict water usage and establish an optimal supply plan every the various event.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2016.04a
• /
• pp.766-769
• /
• 2016

산업시설 가운데 화력발전소에 스마트 부하관제 시스템을 이용하여 메탈 조명을 LED 조명으로 대체하여 전력을 절감하였다. LED 조명의 빛의 세기를 조절 할 수 있는 디밍을 통해서 사용자의 다양한 요구사항을 반영할 수 있다. 수요 예측은 전력 사용량이 많은 시간대의 전력사용량을 분산시키는 시스템이다. 수요 예측 스케줄에 의해서 목표전력량에 도달하기 위해 설정된 시간대에 디밍으로 LED 조명의 전력량을 조절하여, 기존 LED 조명 대비 전력량을 27.8% 절감하였다. 외부에 눈과 비가 오거나 밤에 실내가 어두워져서 작업 환경에 영향을 미치는 경우가 있다. 실시간으로 조도를 측정하여 작업 환경이 원활한 기준조도에 맞도록 LED 조명을 디밍 하여 작업환경을 개선하였다.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2020.11a
• /
• pp.781-783
• /
• 2020

여름철만 되면 폭염 취약계층의 피해 소식이 꾸준히 발생하고 있다. 본 연구는 폭염 취약계층을 예측하기 위한 방법으로 통신사와 공공데이터에서 유동인구데이터, 전기사용량, 온도데이터, 건물 면적, 병원 접근성 등을 활용하여 분석하였다. 디지털 트윈 기법을 활용해 분석결과 높은 온도대비 면적당 전기사용량이 적으며 동시에 유동인구가 많고 병원 접근성이 떨어질수록 폭염 취약계층일 확률이 높을 것으로 예측하였다.

• Journal of the Korea Society of Computer and Information
• /
• v.19 no.8
• /
• pp.19-26
• /
• 2014

Analysis of influential factors on water consumption in households will help predicting the water demand of end-use in household and give an explanation to cause on the change of trend. In this research, the data are gathered by radio telemetry system which is combined electronic flow-meter and wireless communication system in 140 household in Korea. Using this data, we estimate for each residential type to determine liter per capita day. we used real data to predict bathtub and washbowl water-uses and compared the ordinary least square regression model and autoregressive regression error model. The results of this study can be applied in the planning stages of water and waste water facilities.

• Korean Chemical Engineering Research
• /
• v.60 no.1
• /
• pp.25-33
• /
• 2022

This study developed the black liquor evaporation process models using the multiple-effect-evaporator according to the number of effects to predict steam consumption. The developed models were divided into the black liquor preheating and evaporation processes, and a virtual reboiler was added to predict steam consumption. In simulation results, the steam consumption in the double-effect-evaporator was decreased by 48.9 %, and as the number of effects increased, the steam consumption was decreased. Finally, the steam consumption in the octuple-effect-evaporator was decreased by 61.2 %. Also, this study suggests a strategy for deriving the optimal number of effects in the process by analyzing the latent heat recovered from the saturated vapor produced in the multiple-effect-evaporator and the amount of saturated vapor produced by each effect.

• Proceedings of the Korea Information Processing Society Conference
• /
• 2011.04a
• /
• pp.1057-1059
• /
• 2011

본 논문은 홈/빌딩네트워크 환경에서 에너지 절감을 위한 효율적인 관리방법을 제안한다. 보다 상세하게는 먼저 가정(home)이나 빌딩(building) 등의 건물에서 사용되는 에너지의 정보를 수집하여 이들에 관한 통합 자원 관계맵을 구축한다. 구축된 자원 관계맵을 기반으로 실제 에너지 사용량과 금액을 계산하여 알려줄 뿐만 아니라 에너지의 예측 사용량과 금액을 계산하여 사용자에게 알려준다. 이와 더불어 사용량이 많을 경우 각 자원별 뿐만 아니라 지역, 사용량, 타입, 특성과 같은 다양한 그룹별로 에너지 절감 방안을 제시함으로써 사용자가 실제로 에너지를 효과적으로 절감할 수 있도록 하는 자원관계맵 기반의 에너지 절감방안을 제안한다.

• The Magazine of the Society of Air-Conditioning and Refrigerating Engineers of Korea
• /
• v.43 no.8
• /
• pp.42-48
• /
• 2014