• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.27 no.1
• /
• pp.50-56
• /
• 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.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.14 no.2
• /
• pp.153-160
• /
• 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.

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

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.28 no.9
• /
• pp.355-360
• /
• 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.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.16 no.6
• /
• pp.556-565
• /
• 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.

• Journal of the Korean Society of Mechanical Technology
• /
• v.13 no.2
• /
• pp.63-70
• /
• 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.

• Transactions of the Korean Society of Mechanical Engineers B
• /
• v.37 no.12
• /
• pp.1105-1112
• /
• 2013

In this study, a method for predicting heating loads using building characteristic coefficients is proposed for heating system control, and a method for predicting hourly temperature and solar insolation, which mainly affect building heating loads, is also proposed. The temperature and solar insolation are predicted by using a fuzzy theory from forecast information at the meteorological agency, and the building characteristic coefficients for the prediction of heating loads are derived from EnergyPlus. The simulated heating loads of the present study show good agreement with those of EnergyPlus. and the variations of the predicted heating loads using the predicted temperature and solar insolation are similar to those using the actual weather data.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.23 no.5
• /
• pp.334-341
• /
• 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.

• KIPS Transactions on Software and Data Engineering
• /
• v.7 no.4
• /
• pp.129-134
• /
• 2018

Recently, the artificial neural network (ANN) model is a promising technique in the prediction, numerical control, robot control and pattern recognition. We predicted the outside temperature of greenhouse using ANN and utilized the model in greenhouse control. The performance of ANN model was evaluated and compared with multiple regression model(MRM) and support vector machine (SVM) model. The 10-fold cross validation was used as the evaluation method. In order to improve the prediction performance, the data reduction was performed by correlation analysis and new factor were extracted from measured data to improve the reliability of training data. The backpropagation algorithm was used for constructing ANN, multiple regression model was constructed by M5 method. And SVM model was constructed by epsilon-SVM method. As the result showed that the RMSE (Root Mean Squared Error) value of ANN, MRM and SVM were 0.9256, 1.8503 and 7.5521 respectively. In addition, by applying the prediction model to greenhouse heating load calculation, it can increase the income by reducing the energy cost in the greenhouse. The heating load of the experimented greenhouse was 3326.4kcal/h and the fuel consumption was estimated to be 453.8L as the total heating time is $10000^{\circ}C/h$. Therefore, data mining technology of ANN can be applied to various agricultural fields such as precise greenhouse control, cultivation techniques, and harvest prediction, thereby contributing to the development of smart agriculture.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.9 no.1
• /
• pp.1-13
• /
• 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.