• 설비공학논문집
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• 제27권1호
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• pp.50-56
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• 2015

Korea district heating systems have mainly used setting temperature control and outdoor reset control. Different from such conventional normal methods, a thermal-load control proposed in Sweden can decrease the return temperature and reduce pump power consumptions because the control is able to provide the appropriate amount of required heat. In this study, further improved predictive optimal control in addition to the conventional controls were simulated in order to verify its effect in district heating system using TRNSYS 17. $200m^2$ apartment housing which accounts for 25% in Korea and is used as a calculation model;. the number of households in the simulation was 9. As a result, a higher temperature difference and decreasing flow rate at primary loop were shown when using thermal-load control.

• 설비공학논문집
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• 제14권2호
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• pp.153-160
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• 2002

As a way to assess the reliability of programs for building energy analysis, verification experiment and calculation of heating load are simultaneously conducted for a well-defined test space. Experimental conditions are carefully set to minimize uncertainties associated with radiation heating, air change, infiltration, and room-to-room interaction. Dyna- mic load calculations using TRNSYS, which are performed for two different computation domains, rely on the energy rate control that represents inherent load characteristics of a space. The predicted instantaneous heating load favorably simulates the overall behavior the measured one, though the latter fluctuates much more rapidly than the former Comparison of the accumulative load between the experiment and calculations shows a close agreement within an engineering tolerance, regardless of the computation model. It is deduced from such findings that the present experimental results along with weather information can serve as a set of reference data for validating load calculation softwares from the users'standpoint. In order to enhance the completeness of this work, a complementary study on the cooling load for the same test space is highly recommended.

• 대한기계학회논문집B
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• 제36권8호
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• pp.803-809
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• 2012

지역난방 시스템의 최적 스케쥴 제어를 위해서는 난방부하 예측이 필요하다. 공동주택의 난방부하는 복잡한 변수들의 영향을 받기 때문에 손쉬운 난방부하 예측을 위해 사용하기 쉬우며 효용성 있는 예측방법의 개발이 필요하다. 본 연구에서는 익일의 시간별 난방부하를 예측하기 위해 단순화된 외기조건 예측방법과 부하 예측방법을 제안하였다. 난방부하 예측을 위해 건물설계서에서 쉽게 얻을 수 있는 간단한 사양과 예측된 온습도가 사용되었다. 제안된 방법의 타당성을 검증하기 위해 지역난방 시스템으로부터 시간별로 실측된 난방부하와 예측된 결과를 비교하였다. 예측된 외기조건은 실측된 값과 비교하여 변화양상이 잘 일치하였다. 예측된 난방부하와 측정된 난방부하를 비교한 결과, 시간별, 일별, 월별 모두 예측과 실측이 비교적 잘 일치하였으며, 난방기간 동안 월별 부하의 평균 오차는 약 4.68%로 비교적 작은 값을 가졌다.

• 설비공학논문집
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• 제28권9호
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• pp.355-360
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• 2016

Continuing to the modeling of heating load, this paper, as the second part of consecutive works, presents LS-SVM (least square support vector machine) based model of winter time apartment hot water supply load in a district heating system, so as to be used in prediction of heating energy usage. Similar, but more severely, to heating load, hot water supply load varies in highly nonlinear manner. Such nonlinearity makes analytical model of it hardly exist in the literatures. LS-SVM is known as a good modeling tool for the system, especially for the nonlinear system depended by many independent factors. We collect 26,208 data of hot water supply load over a 13-week period in winter time, from 12 heat exchangers in seven different apartments. Then part of the collected data were used to construct LS-SVM based model and the rest of those were used to test the formed model accuracy. In modeling, we first constructed the model of district heating system's hot water supply load, using the unit heating area's hot water supply load of seven apartments. Such model will be used to estimate the total hot water supply load of which the district heating system needs to provide. Then the individual apartment hot water supply load model is also formed, which can be used to predict and to control the energy consumption of the individual apartment. The results obtained show that the total hot water supply load, which will be provided by the district heating system in winter time, can be predicted within 10% in MAPE (mean absolute percentage error). Also the individual apartment models can predict the individual apartment energy consumption for hot water supply load within 10% ~ 20% in MAPE.

• 설비공학논문집
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• 제16권6호
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• pp.556-565
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• 2004

We have experimented an actual solar water heating system acquiring real data for one year period. On the basis of the operation data, it is necessary to predict the system performance such as collector efficiency and solar fraction, and to analyze the economical efficiency for system optimal design. To estimate the performance of actual systems through simulation, valid modelling for components consisting of the system should be accompanied. The present study is focused on the modelling for load patterns and operating control conditions. We proposed two load models: concentration model which gathers real loads as a meaningful group and distribution model which disperses real loads with time. If grouping of the load distribution is suitable, the predicted values by the concentration model approaches to those by the distribution model close to actual load pattern apparently. As a result, both of them are in good agreement with those by experiment.

• 한국기계기술학회지
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• 제13권2호
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• pp.63-70
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• 2011

In the present study, we developed optimal heat supply algorithm which minimizes the heat loss through the distribution pipe line in group energy apartment. Heating load variation of group energy apartment building in accordance with outdoor air temperature was predicted by the correlation obtained from calorimeter measurements of whole households of apartment building. Supply water temperature and mass flow rate were conjugately controlled to minimize the heat loss rate through distribution pipe line. Group heating apartment located in Hwaseong city, Korea, which has 1,473 households divided in 4 regions, was selected as the object apartment for verifying the present heat supply control algorithm. Compared to the original heat supply system, 10.4% heat loss rate reduction can be accomplished by employing the present control algorithm.

• 대한기계학회논문집B
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• 제37권12호
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• pp.1105-1112
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• 2013

본 연구에서는 난방설비 제어에 필요한 난방부하를 건물 특성계수를 사용하여 예측하는 방법을 제안하였고, 난방부하에 주된 영향을 미치는 시간별 온도와 일사량을 예측하는 방법을 제안하였다. 온도와 일사량은 기상청에서 예보되는 정보로부터 퍼지이론을 이용하여 예측하였고, 난방부하 예측을 위한 건물 특성계수는 EnergyPlus로부터 도출하였다. 본 연구에서 제안된 방법으로 얻어진 난방부하는 EnergyPlus의 결과와 잘 일치하였으며, 예측된 온도와 일사를 이용하여 예측한 난방부하의 변화 양상은 실측 기상데이터를 사용한 결과와 유사하였다.

• 설비공학논문집
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• 제23권5호
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• pp.334-341
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• 2011

In the present study, optimal heat supply algorithm which minimize the heat loss through the distribution pipe line in group energy apartment was developed. Variation of heating load of group energy apartment building in accord with the outdoor air temperature was predicted by the heating load-outdoor temperature correlation. Supply water temperature and mass flow rate were controlled to minimize the heat loss through distribution pipe line. District heating apartment building located in Hwaseong city, which has 1,473 households, was selected as the object building for testing the present heat supply a1gorithm. Compared to the previous heat supply system, 10.4% heat loss reduction can be accomplished by employing the present method.

• 정보처리학회논문지:소프트웨어 및 데이터공학
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• 제7권4호
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• pp.129-134
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• 2018

최근, 인공신경망 모델은 예측, 수치제어, 로봇제어, 패턴인식 등의 분야에서 촉망되는 기술이다. 본 연구에서는 인공신경망 모델을 이용하여 온실 외부 온도를 예측하고 이를 온실제어에 활용하는데 목적이 있다. 예측 모델의 성능 평가를 위해 다중회귀모델과 SVM 모델과의 비교분석을 수행하였다. 평가 방법으로는 10-Fold Cross Validation을 사용하였으며, 예측 성능 향상을 위해 상관관계분석 통해 데이터 축소를 수행하였고, 측정 데이터로부터 새로운 Factor 추출하여 데이터의 신뢰성을 확보하였다. 인공신경망 구축을 위해 Backpropagation algorithm을 사용하였으며, 다중회귀모델은 M5 method로 구축하였고, SVM 모델을 epsilon-SVM으로 구축하였다. 각 모델의 비교분석 결과 각각 0.9256, 1.8503과 7.5521로 나타났다. 또한 예측모델을 온실 난방부하 계산에 적용함으로써 온실에 사용되는 에너지 비용 절감을 통한 수입증대에 기여할 수 있다. 실험한 온실의 난방부하는 3326.4kcal/h이며, 총 난방시간이 $10000^{\circ}C/h$일 때 연료소비량은 453.8L로 예측된다. 아울러 데이터 마이닝 기술 중 하나인 인공신경망을 정밀온실제어, 재배기법, 수확예측 등 다양한 농업 분야에 적용함으로써 스마트 농업으로의 발전에 기여할 수 있다.

• 설비공학논문집
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• 제9권1호
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• pp.1-13
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• 1997

This paper reports the dynamic analysis of energy consumption for an office building by heat resistance-capacitance method. If a building is divided into several wall components and the wall components is replaced by one thermal capacitance and several thermal resistances, the building becomes an electric circuit. By solving the simultaneous equations of the circuit, the dynamic heat transfer characteristics and the energy consumption rate of the building were predicted. Accuracy of modified BIN method was evaluated by the present resistance-capacitance method. The result shows that modified BIN method overpredicts the heating load of the office building 15%. Annual energy consumptions of equipments(fan, boiler, chiller) for various ventilating control system(CAV, VAV, FCU+VAV, FCU+CAV) were compared. FCU+CAV shows the minimum annual energy consumption.