• Journal of the KSME
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• v.32 no.3
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• pp.247-254
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• 1992

이 글에서는 고충아파트에서 층간 열공급의 불균일성이 발생하는 원인을 설명하였고 이 불균일 성을 해소시키는 새로운 열공급 설비 기술을 설명하였으며 그 내용들에 대한 결론은 다음과 같다. (1) 온수 난방 고층아파트의 층간 열공급의 불균일성은 온수공급관과 회수관의 정수압력의 차 이에 의한 유동저항에 의해서 발생한다. (2) 다구역배관망 시스템은 온수공급관과 회수관의 정수압력의 차이에 의해 발생하는 열공급 불균일성의 해소에 거의 기여하지 못한다. (3) 연속난방방식에서 서모스타트의 설치는 각세대의 열공급을 균일하게 할 수 있고 에너지 절 약면에서 가장 유리한 방식이나 고장의 위험성이 가장 크며, 운전의 미숙에 의한 열공급 제어의 불균일성을 초래하기 쉬우며 외기온도가 매우 낮으면 층간 열공급제어가 되지 않을 수 있어서 자동유량조절밸브를 병용하거나 온수공급압력을 충분히 크게 할 필요가 있다. (4) 자동유량조절밸브에 부착하면 단구역 배관망 시스템에서도 층간의 유량공급의 불균일성을 없앨 수 있으나 순환 펌프유량이 부족하면 밸브의 자동유량조절 기능이 상실된다. (5) 수동유량조절밸브는 TAB에 의해서 충간의 유량을 균일하게 조절할 수 있으나 펌프 성능변화 관부식 등이 발생하면 TAB를 다시 할 필요가 발생할 수 있다. 그러나 밸브의 고장이 적고 유 동저항을 적게 발생시키며 한 개의 밸브로 조절할 수 있는 유량의 범위가 큰 점에서는 가장 유 리하다.

• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 2002.11a
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• pp.265-270
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• 2002

본 연구에서는 AHP(Analytic Hierarchy Process)를 이용한 열 흐름상 에너지시스템의 평가와 함께 그 결과로서 시스템 대안들에 대한 우선순위를 제시하였으며 이를 통해 열공급 시스템에 대한 최적 의사결정을 도출할 수 있는 평가모형을 정립코자 하였다. 일반적으로 AHP 방법은 복잡한 의사결정 문제를 체계적으로 세분하여 분석할 수 있도록 하는 도구를 제공한다.(중략)

• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 2001.11a
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• pp.111-116
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• 2001

• Journal of the Korean Society of Mechanical Technology
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• v.13 no.2
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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.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.23 no.5
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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.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.36 no.8
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• pp.803-809
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• 2012

It is necessary to predict the heating load in order to determine the optimal scheduling control of district heating systems. Heating loads are affected by many complex parameters, and therefore, it is necessary to develop an efficient, flexible, and easy to use prediction method for the heating load. In this study, simple specifications included in a building design document and the estimated temperature and humidity are used to predict the heating load on the next day. To validate the performance of the proposed method, heating load data measured from a benchmark district heating system are compared with the predicted results. The predicted outdoor temperature and humidity show a variation trend that agrees with the measured data. The predicted heating loads show good agreement with the measured hourly, daily, and monthly loads. During the heating period, the monthly load error was estimated to be 4.68%.

• Journal of Energy Engineering
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• v.28 no.4
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• pp.82-93
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• 2019

In this study, a new model is proposed to improve the problem of the decline of predict rate of heat demand on a particular date, such as a public holiday for the conventional heat demand forecasting system. The proposed model was the Four Season Mixed Heat Demand Prediction Neural Network Model, which showed an increase in the forecast rate of heat demand, especially for each type of forecast date (weekday/weekend/holiday). The proposed model was selected through the following process. A model with an even error for each type of forecast date in a particular season is selected to form the entire forecast model. To avoid shortening learning time and excessive learning, after each of the four different models that were structurally simplified were learning and a model that showed optimal prediction error was selected through various combinations. The output of the model is the hourly 24-hour heat demand at the forecast date and the total is the daily total heat demand. These forecasts enable efficient heat supply planning and allow the selection and utilization of output values according to their purpose. For daily heat demand forecasts for the proposed model, the overall MAPE improved from 5.3~6.1% for individual models to 5.2% and the forecast for holiday heat demand greatly improved from 4.9~7.9% to 2.9%. The data in this study utilized 34 months of heat demand data from a specific apartment complex provided by the Korea District Heating Corp. (January 2015 to October 2017).