• Journal of the Korean Solar Energy Society
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• v.29 no.3
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• pp.37-44
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• 2009

The field-surveyed and measured energy consumption data is processed to develop building energy demand models for heating, hot water, cooling, and electricity. The load models are systematically organized as a database and hourly loads for a span of year (8760 hours) are generated by the program. Rased on those models a Microsoft Access application program is developed to calculate energy demands for a Community Energy System (CES) composed of 17 types of buildings. User-friendly interfaces are provided to assist non-expert end users and necessary tools to link the calculation results to subsequent coagulations such as operation simulation or economic assessment.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.28 no.4
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• pp.262-270
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• 2015

In this paper existing buildings, not a new buildings and house for living people not just a displaying and a viewing, created by the imagine effect or virtual simulation was applied various Active and Passive elements. After constructing zero-energy houses, through default case happened during operation period it is described problems and solutions about field part, work classification, installation by Location part, and Installation equipment part. Since then, to take advantage of this thesis, it's the purpose of this paper using as the baseline data for building a zero-energy house in another similar case.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.11
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• pp.42-50
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• 2014

In the thesis, the case of zero energy house construction applied with various Active factors and Passive factors which is the real residence as a standard not a normal experimental residence was evaluated, analyzed, and organized. The thesis can be the base data to construct another similar case of zero energy house.

• Journal of the Korean Solar Energy Society
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• v.36 no.6
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• pp.61-69
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• 2016

In this study, we analyzed five-year(2011~2015) data for D high school in Seoul area to analyze energy consumption characteristics in high school. The results are summarized as follows. (1) In the result of comparison analysis about 2015 energy consumption by usage, based on primary energy, 18% of energy was consumed in cafeteria, and 82% was consumed in main building. In the case of main building, base and constant load excepting hot water supply in restroom took 40%, heating including freeze protection took 20%, hot water supply in restroom took 14%, and cooling took 8% in order. (2) In the 2015 total energy consumption in D high school based on primary energy, heating energy takes 28%. The range and limit of energy savings coming from the reinforcement of insulation and window performance could be estimated. (3) To introduce new & renewable energy system in high school, electricity-based system is suitable than heat-based system because usage of electric energy is larger than that of heat energy in high school. (4) Five-year energy consumption unit according to heating degree-day showed a linearly increasing trend, and the coefficient of determination(R2) was 0.9763, which means high correlation.

• KIEAE Journal
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• v.13 no.2
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• pp.77-84
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• 2013

Schools are one of favorable public buildings for Renewable Energy(RE) systems due to site conditions and their energy demand profiles(e.g. daytime-based use of hot water and heating/cooling). Although the government encourages schools to be equipped with RE systems, the adoption of RE systems in existing energy supply systems faces technical and financial barriers. For example, when installing a RE-based combination system(RECS) to meet the energy demand at various school scales, identifying cost effective combination of capacities of the RECS is not trivial since it usually requires technically intensive work including detailed simulation and demand/supply analysis with extensive data. This kind of simulation-based approaches is hardly implementable in practice. To address this, a simpler and applicable decision-supporting method is suggested in this study. This paper presents a simplified model in support of decision-making for optimal capacities of RECS within given budget scales and schools sizes. The proposed model was derived from detailed simulation results and statistical data. Using this model, the optimal capacities of RECS can be induced from the number of classes in a school.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.7
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• pp.616-622
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• 2016

The heating load using solar hot water is lower in summer than in the other seasons. This decreased heating load leads to the overheating solar collectors and related components. To prevent overheating of the solar collectors, air cooling and shading shields were used. On the other hand, it requires additional mechanical components, and reduces the system reliability. The geothermal heat dump system to release the high temperature heat (over $150^{\circ}C$) transferred from the heat pipe solar collectors was investigated in the present study. Research on the heat dump to cool the solar collector is rare. Therefore, the present study was carried out to collect possible data of a geothermal heat dump to cool the solar collector. A helical type geothermal heat exchanger was buried at a 1.2m depth. Experimentally and numerically, the geothermal heat dump was investigated in terms of the effects of parameters, such as the quantity of solar radiation, aperture area of the collector and the mass flow rate. A pipe length of 50m on the geothermal heat exchanger was suitable with a 0.33 kg/s flow rate. The water reservoir was a possible co-operation solution linked to the geothermal heat exchanger.

• 한국태양에너지학회:학술대회논문집
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• 2009.04a
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• pp.127-132
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• 2009

A Microsoft Access application program is developed to calculate energy demands for a Community Energy System (CES) composed of various types of buildings. The field-measured heating, hot water, cooling, and electricity energy consumptions for 14 types of building are systematically organized in forms of database and hourly loads for a span of year (8760 hours) are generated through an automated statistical procedure. User-friendly standard windows interfaces are provided to assist non-expert end users.

• 한국태양에너지학회:학술대회논문집
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• 2009.04a
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• pp.184-189
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• 2009

In order to improve the operation of energy systems, it is necessary for the urban communities to have reliable optimization routines, both computerized and manual, implemented in their organizations. However, before a production plan for the energy system units can be constructed, a prediction of the energy systems first needs to be determined. So, several methodologies have been proposed for energy demand prediction, but due to uncertainties in urban community, many of them will fail in practice. The main topic of this paper has been the development of a method for energy demand prediction at urban community. Energy demand prediction is important input parameters to plan for the energy planing. This paper presents a energy demand prediction method which estimates heat and electricity for various building categories. The method has been based on artificial neural networks(ANN). The advantage of ANN with respect to the other method is their ability of modeling a multivariable problem given by the complex relationships between the variables. Also, the ANN can extract the relationships among these variables by means of learning with training data. In this paper, the ANN have been applied in oder to correlate weather conditions, calendar data, schedules, etc. Space heating, cooling, hot water and HVAC electricity can be predicted using this method. This method can produce 10% of errors hourly load profile from individual building to urban community.

• Journal of the Korean Wood Science and Technology
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• v.2 no.2
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• pp.32-34
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• 1974

The important results which have been obtained in the investigation can be recapitulated as follows. 1. As demonstrated by the experimental results and analyses concerning their effects in the on-ground type mushroom house, the constructions in relation to the side wall and ceiling of the experimental house showed a sufficient heat insulation on effect to protect insides of the house from outside climatic conditions. 2. As the effect on the solar type experimental mushroom house which was constructed in a half basement has been shown by the experimental results and analyses, it has been proved to be effective for making use of solar heat. However there were found two problems to be improved for putting solar house to practical use in the farm mushroom growing: (1) the construction of the roof and ceiling should be the same as for the on ground type house, and (2) the solar heat generating system should be reconstructed properly. 3. Among several ventilation systems which have been studied in the experiments, the underground earthen pipe and ceiling ventilation, and vertical side wall and ceiling ventilation systems have been proved to be most effective for natural ventilation. 4. The experimental results have shown that ventilation systems such as the vertical side wall and underground ventilation systems are suitable to put to practical use as natural ventilation systems for farm mushroom house. These ventilation systems can remarkably improve the temperature of fresh air which is introduced into the house by heat transfers within the ventilation passages, so as to approach to the desired temperature of the house without any cooling or heating operation. For example, if it is assuming that X is the outside temperature and Y is the amount of temperature adjustment made by the influence of the ventilation system, the relationships that exist between X and Y can be expressed by the following regression lines. Underground iron pipe ventilation system. Y=0.9X-12.8 Underground earthen pipe ventilation system. Y=0.96X-15.11 Vertical side wall ventilation system. Y=0.94X-17.57 5. The experimental results have 8hown that the relationships existing between the admitted and expelled air and the $CO_2$ concentration can be described with experimental regression lines or an exponent equation as follows: 5.1 If it is assumed that X is an air speed cm/sec. and Y is an expelled air speed in cm/sec. in a natural ventilation system, since the Y is a function of the X, the relationships that exist between X and Y can be expressed by the regression lines shown below: 5.2 If it IS assumed that X is an admitted volume of air in $m^3$/hr. and Y is an expelled volume of air in $m^3$/hr. in a natural ventilation system, since the Y is a function of the X, the relationships that exist between X and Y can be expressed by the regression lines shown below. 5.3 If it is assumed that expelled air speed in emisec. and replacement air speed in cm/sec. at the bed surface in a natural ventilation system are shown as X and Y. respectively, since the Y is a function of the X. the relationships that exist between X and Y can be expressed by the following regression line: GE(100%)-CV (50%) ventilation system. Y=-0.54X+0.84 5.4 If it is assumed that the replacement air speed in cm/sec. at the bed surface is shown as X, and $CO_2$ concentration which is expressed by multiplying 1000 times the actual value of $CO_2$ % is shown as Y, in a natural ventilation system, since the Y is a function of the X, the relationships that exist between X and Y can be expressed by the following regression line: GE(100%)-CV(50%) ventilation system. Y=114.53-6.42X 5.5 If it is assumed that the expelled volume of air is shown as X and the $CO_2$ concencration which is expressed by multiplying 1000 times the actual of $CO_2$% is shown as Y in a natural ventilation system, since the Y is a function of the X, the relationships that exist between X and Y can be expressed by the following exponent equation: GE(100%)-CV(50%) ventilation system. Y=$127.18{\times}1.0093^{-x}$ 5.6 The experimental results have shown that the ratios of the cross sectional area of the GE and CV vent to the total cubic capacity of the house, required for providing an adequate amount of air in a natural ventilation system, can be estimated as follows: GE(admitting vent of the underground ventilation) 0.3-0.5% (controllable) CV(expelling vent of the ceiling ventilation) 0.8-1.0% (controllable) 6. Among several heating devices which were studied in the experiments, the hot-water boilor which wasmodified to be fitted both as hot-water boiler and as a pressureless steam-water was found most suitable for farm mushroom growing.

• Journal of Bio-Environment Control
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• v.5 no.2
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• pp.215-235
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• 1996

The thermal analysis by mathematical model simulation makes it possible to reasonably predict heating and/or cooling requirements of certain greenhouses located under various geographical and climatic environment. It is another advantages of model simulation technique to be able to make it possible to select appropriate heating system, to set up energy utilization strategy, to schedule seasonal crop pattern, as well as to determine new greenhouse ranges. In this study, the control pattern for greenhouse microclimate is categorized as cooling and heating. Dynamic model was adopted to simulate heating requirements and/or energy conservation effectiveness such as energy saving by night-time thermal curtain, estimation of Heating Degree-Hours(HDH), long time prediction of greenhouse thermal behavior, etc. On the other hand, the cooling effects of ventilation, shading, and pad ||||&|||| fan system were partly analyzed by static model. By the experimental work with small size model greenhouse of 1.2m$\times$2.4m, it was found that cooling the greenhouse by spraying cold water directly on greenhouse cover surface or by recirculating cold water through heat exchangers would be effective in greenhouse summer cooling. The mathematical model developed for greenhouse model simulation is highly applicable because it can reflects various climatic factors like temperature, humidity, beam and diffuse solar radiation, wind velocity, etc. This model was closely verified by various weather data obtained through long period greenhouse experiment. Most of the materials relating with greenhouse heating or cooling components were obtained from model greenhouse simulated mathematically by using typical year(1987) data of Jinju Gyeongnam. But some of the materials relating with greenhouse cooling was obtained by performing model experiments which include analyzing cooling effect of water sprayed directly on greenhouse roof surface. The results are summarized as follows : 1. The heating requirements of model greenhouse were highly related with the minimum temperature set for given greenhouse. The setting temperature at night-time is much more influential on heating energy requirement than that at day-time. Therefore It is highly recommended that night- time setting temperature should be carefully determined and controlled. 2. The HDH data obtained by conventional method were estimated on the basis of considerably long term average weather temperature together with the standard base temperature(usually 18.3$^{\circ}C$). This kind of data can merely be used as a relative comparison criteria about heating load, but is not applicable in the calculation of greenhouse heating requirements because of the limited consideration of climatic factors and inappropriate base temperature. By comparing the HDM data with the results of simulation, it is found that the heating system design by HDH data will probably overshoot the actual heating requirement. 3. The energy saving effect of night-time thermal curtain as well as estimated heating requirement is found to be sensitively related with weather condition: Thermal curtain adopted for simulation showed high effectiveness in energy saving which amounts to more than 50% of annual heating requirement. 4. The ventilation performances doting warm seasons are mainly influenced by air exchange rate even though there are some variations depending on greenhouse structural difference, weather and cropping conditions. For air exchanges above 1 volume per minute, the reduction rate of temperature rise on both types of considered greenhouse becomes modest with the additional increase of ventilation capacity. Therefore the desirable ventilation capacity is assumed to be 1 air change per minute, which is the recommended ventilation rate in common greenhouse. 5. In glass covered greenhouse with full production, under clear weather of 50% RH, and continuous 1 air change per minute, the temperature drop in 50% shaded greenhouse and pad ＆ fan systemed greenhouse is 2.6$^{\circ}C$ and.6.1$^{\circ}C$ respectively. The temperature in control greenhouse under continuous air change at this time was 36.6$^{\circ}C$ which was 5.3$^{\circ}C$ above ambient temperature. As a result the greenhouse temperature can be maintained 3$^{\circ}C$ below ambient temperature. But when RH is 80%, it was impossible to drop greenhouse temperature below ambient temperature because possible temperature reduction by pad ||||&|||| fan system at this time is not more than 2.4$^{\circ}C$. 6. During 3 months of hot summer season if the greenhouse is assumed to be cooled only when greenhouse temperature rise above 27$^{\circ}C$, the relationship between RH of ambient air and greenhouse temperature drop($\Delta$T) was formulated as follows : $\Delta$T= -0.077RH＋7.7 7. Time dependent cooling effects performed by operation of each or combination of ventilation, 50% shading, pad ＆ fan of 80% efficiency, were continuously predicted for one typical summer day long. When the greenhouse was cooled only by 1 air change per minute, greenhouse air temperature was 5$^{\circ}C$ above outdoor temperature. Either method alone can not drop greenhouse air temperature below outdoor temperature even under the fully cropped situations. But when both systems were operated together, greenhouse air temperature can be controlled to about 2.0-2.3$^{\circ}C$ below ambient temperature. 8. When the cool water of 6.5-8.5$^{\circ}C$ was sprayed on greenhouse roof surface with the water flow rate of 1.3 liter/min per unit greenhouse floor area, greenhouse air temperature could be dropped down to 16.5-18.$0^{\circ}C$, whlch is about 1$0^{\circ}C$ below the ambient temperature of 26.5-28.$0^{\circ}C$ at that time. The most important thing in cooling greenhouse air effectively with water spray may be obtaining plenty of cool water source like ground water itself or cold water produced by heat-pump. Future work is focused on not only analyzing the feasibility of heat pump operation but also finding the relationships between greenhouse air temperature(T$_{g}$ ), spraying water temperature(T$_{w}$ ), water flow rate(Q), and ambient temperature(T$_{o}$).