• Journal of Institute of Control, Robotics and Systems
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• v.6 no.6
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• pp.441-446
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• 2000

This paper presents an approach for the prediction of optimal heating load using a diagonal recurrent neural networks(DRNN) and data base system of outdoor temperature. In the DRNN, a dynamic backpropagation(DBP) with delta-bar-delta teaming method is used to train an optimal heating load identifier. And the data base system is utilized for outdoor temperature prediction. Compared to other kinds of methods, the proposed method gives better prediction performance of heating load. Also a hardware for the controller is developed using a microprocessor. The experimental results show that prediction enhancement for heating load can be achieved with the proposed method regardless of the its inherent nonlinearity and large time constant.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.27 no.6
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• pp.283-292
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• 2015

A model of apartment heating load in a district heating system could be useful in the management and utilization of energy resources, since it could predict energy usage and so could assist in the efficient use of energy resources. The heating load in a district heating system varies in a highly nonlinear manner and is subject to many different factors, such as heating area, number of people living in that complex, and ambient temperature. Thus there are few published papers with accurate models of heating load, especially in domestic literature. This work is concerned with the modeling of apartment heating load in a district heating system in winter, using the reduced least square support vector machine (LS-SVM), and with the purpose of using the model to predict heating energy usage in domestic city area. We collected 23,856 pieces of data on heating energy usage over a 12-week period in winter, from 12 heat exchangers in five apartments. Half of the collected data were used to construct the heating load model, and the other half were used to test the model's accuracy. The model was able to predict the heating energy usage pattern rather accurately. It could also estimate the usage of heating energy within of mean absolute percentage error. This implies that the model prediction accuracy needs to be improved further, but it still could be considered as an acceptable model if we consider the nonlinearity and uncertainty of apartment heating energy usage in a district heating system.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.28 no.9
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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.

• 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%.

• The Journal of Sustainable Design and Educational Environment Research
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• v.11 no.2
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• pp.19-27
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• 2012

This study was carried out in order to establish the estimation equation for school power consumption using regression analysis based on collected power consumption for two years of weather data and schools are located in Central Changwon and Masan district in Changwon city. (1) The power consumption estimation equation for Heating and cooling is calculated using power consumption per unit volume, the difference between actual power consumption and results of estimation equations is 4.1%. (2) The power consumption estimation equation for heating load is showed 2.6% difference compared to actual power consumption in Central Changwon and is expressed 2.9% difference compared to that in Masan district. Therefore, the power consumption prediction for each school using the power consumption estimation equation is possible. (3) The power consumption estimation equation for cooling load is showed 8.0% difference compared to actual power consumption in Central Changwon and is expressed 2.9% compared to that in Masan district. As the power consumption estimation equation for cooling load is expressed difference compared to heating load, it needs to investigate influence for cooling load.

• International Journal of Air-Conditioning and Refrigeration
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• v.10 no.2
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• pp.65-71
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• 2002

The objective of this study is to evaluate the design parameters and to develop the cooling and heating load prediction equations of office buildings. The building load calculation simulation was carried out using the DOE-2.1E program. The results of the simulation were used as data for multiple regression analysis which could develop the load prediction equations.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.13 no.9
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• pp.914-921
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• 2001

The objective of this study is to evaluate the design parameters and to develop the cooling and heating load prediction equations of office buildings. The building load calculation simulation was carried out using the DOE-2.1E program. The results of the simulation was used as a data for ANOVA and multiple regression analysis which could develop the load prediction equations.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.8 no.5
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• pp.108-114
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• 1999

The thermal environmental chamber has been using in maintaining weather condition keeping thermal capacity under heating and cooling load fluctuation and for the performance testing of cooling system or air-conditioner on artificial envi-ronment. In ordder to make the various environmental conditions in the thermal environmental chamber the proper cooling system is necessary to eliminate the heating load produced inside the chamber and to maintain the designed environmental condition. For this reason the optimal design of cooling system and the prediction of performance is also required. This paper describes the prediction of performance of cooling system in the thermal environmental chamber with the capacity of 37,000kcal/hr which is developed for the test of performance in heating mode of heat pump system, In the results this paper is trying to develop simulation program on the base of mathematical models and which can be applied effectively to the optimal design of cooling system and prediction of performance to the inside and outside change of envi-ronmetal load.

• Journal of the Korean Solar Energy Society
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• v.31 no.5
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• pp.27-32
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• 2011

Most of domestic building generally don't have fixed shading devices considering of appearance and aesthetic issues. In this study is suggested that tilt window simultaneously has a role of shading and blocking solar radiation. The tilt window thermal performance is investigated by relation ship between inclination and heating cooling road. As comparing vertical window with $5^{\circ}$ and $7^{\circ}$ of tilt window respectively, the heating load is increased by 3.6% and cooling load is reduced by 8.1% on $5^{\circ}$ tilt window and the heating load is increased by 5.3% and cooling load is reduced by 11.5% on $5^{\circ}$ tilt window. Especially, the total load of alternative tilt window is showed the reduction rate 2.6% and3.6% compared of vertical window. Therefore, the tilt window is possible to role of shading of solar radiation and reduction of heating and cooling load.

• Proceedings of the SAREK Conference
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• 2009.06a
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• pp.1300-1305
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• 2009

The energy conservation in buildings affects environmental preservation as well as economic benefits, and creates the comfortable indoor environment set for the inhabitants. Especially, apartment buildings show ever-increasing energy consumption with large-sized and high-class tendency, thus energy saving counterplans are needed. The present study is to develop an optical control algorithm by using heating load curve according to the outdoor temperature change. Heating load analysis should be performed before the present method can be applied. Dynamic heating load simulations are performed by resistance-capacitance method. Results show that heating load decrease linearly according to the increase of outdoor temperature.