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

• KIEAE Journal
• /
• v.14 no.4
• /
• pp.111-117
• /
• 2014

In South Korea, the government has recently enforced regulations associated with buildings. Temperature restriction in indoor environment is one of the common ways of energy reduction in order not to dissipate heating and cooling energy; however the people who are in restricted temperature feels uncomfortable. Furthermore, occupants cannot feel the same thermal sensation even they are in the same place. For the reason, occupants should express their thermal sensation and HVAC system should be able to apply their demand. It is proved by an adaptive principle. The adaptive model means that people react in ways which tend to restore their comfort, when change occurs such as to produce discomfort. In order to design HVAC control strategies based on adaptive model, we designated an existing office building as a reference building to gather data from actual field. Furthermore, we gathered occupants' thermal sensation and clothing insulation in real-time. We filtered the data with Kalman's filter method. The data was reasonable when there is an alarm messages for asking questionnaire. The response ratio were different in occupants' thermal condition. In conclusion, the filtered occupants' thermal sensation can be used as a real time HVAC control and input value of HVAC control.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.18 no.6
• /
• pp.183-190
• /
• 2004

This paper proposes a methodology to solve problems upon the circuit design applied to inductor load by applying a circuit to improve power factor with is partial switching PFC module to the power supply system for cooling/heating inverter air conditioner and by designing an input power section in compliance with IEC555-2 on the basis of better input power factor and minimized harmonic components of current. On the other hand, this paper suggested how to control the increase of output voltage along with tぉw current waves and partial switching PFC circuit as well, which can provide the output as twice as input voltage This study applied a method to control the compressors of air conditioner by means of increased the voltage applicable to compressor motor by lowering switching number conclusively, it could solve questions about efficiency, economics, electronic noise and so forth. and so that the reasonable voltage for running moor could be set up along with lower power consumption of air conditioner than estimated It was demonstrated that total sum of energy efficiency to operate system was increased to the extent of valid level. And all this merits and appropriateness was proved by computer simulation and experience.

• Journal of Bio-Environment Control
• /
• v.31 no.3
• /
• pp.246-254
• /
• 2022

Domestic facility agriculture grows rapidly, such as modernization and large-scale. And the production scale increases significantly compared to the area, accounting for about 60% of the total agricultural production. Greenhouses require energy input to create an appropriate environment for stable mass production throughout the year, but the energy load per unit area is large because of low insulation properties. Through the rooftop greenhouse, one of the types of urban agriculture, energy that is not discarded or utilized in the building can be used in the rooftop greenhouse. And the cooling and heating load of the building can be reduced through optimal greenhouse operation. Dynamic energy analysis for various environmental conditions should be preceded for efficient operation of rooftop greenhouses, and about 40% of the solar energy introduced in the greenhouse is energy exchange for crops, so it should be considered essential. A major analysis is needed for each sensible heat and latent heat load by leaf surface temperature and evapotranspiration, dominant in energy flow. Therefore, an experiment was conducted in a rooftop greenhouse located at the Korea Institute of Machinery and Materials to analyze the energy exchange according to the growth stage of crops. A micro-meteorological and nutrient solution environment and growth survey were conducted around the crops. Finally, a regression model of leaf temperature and evapotranspiration according to the growth stage of leafy vegetables was developed, and using this, the dynamic energy model of the rooftop greenhouse considering heat transfer between crops and the surrounding air can be analyzed.

• Korean Journal of Soil Science and Fertilizer
• /
• v.44 no.6
• /
• pp.1195-1206
• /
• 2011

This study was conducted to apply LCA (Life cycle assessment) methodology to lettuce (Lactuca sativa L.) production systems in Namyang-ju as a case study. Five lettuce growing farms with three different farming systems (two farms with organic farming system, one farm with a system without agricultural chemicals and two farms with conventional farming system) were selected at Namyangju city of Gyeonggi-province in Korea. The input data for LCA were collected by interviewing with the farmers. The system boundary was set at a cropping season without heating and cooling system for reducing uncertainties in data collection and calculation. Sensitivity analysis was carried out to find out the effect of type and amount of fertilizer and energy use on GHG (Greenhouse Gas) emission. The results of establishing GTG (Gate-to-Gate) inventory revealed that the quantity of fertilizer and energy input had the largest value in producing 1 kg lettuce, the amount of pesticide input the smallest. The amount of electricity input was the largest in all farms except farm 1 which purchased seedlings from outside. The quantity of direct field emission of $CO_2$, $CH_4$ and $N_2O$ from farm 1 to farm 5 were 6.79E-03 (farm 1), 8.10E-03 (farm 2), 1.82E-02 (farm 3), 7.51E-02 (farm 4) and 1.61E-02 (farm 5) kg $kg^{-1}$ lettuce, respectively. According to the result of LCI analysis focused on GHG, it was observed that $CO_2$ emission was 2.92E-01 (farm 1), 3.76E-01 (farm 2), 4.11E-01 (farm 3), 9.40E-01 (farm 4) and $5.37E-01kg\;CO_2\;kg^{-1}\;lettuce$ (farm 5), respectively. Carbon dioxide contribute to the most GHG emission. Carbon dioxide was mainly emitted in the process of energy production, which occupied 67~91% of $CO_2$ emission from every production process from 5 farms. Due to higher proportion of $CO_2$ emission from production of compound fertilizer in conventional crop system, conventional crop system had lower proportion of $CO_2$ emission from energy production than organic crop system did. With increasing inorganic fertilizer input, the process of lettuce cultivation covered higher proportion in $N_2O$ emission. Therefore, farms 1 and 2 covered 87% of total $N_2O$ emission; and farm 3 covered 64%. The carbon footprints from farm 1 to farm 5 were 3.40E-01 (farm 1), 4.31E-01 (farm 2), 5.32E-01 (farm 3), 1.08E+00 (farm 4) and 6.14E-01 (farm 5) kg $CO_2$-eq. $kg^{-1}$ lettuce, respectively. Results of sensitivity analysis revealed the soybean meal was the most sensitive among 4 types of fertilizer. The value of compound fertilizer was the least sensitive among every fertilizer imput. Electricity showed the largest sensitivity on $CO_2$ emission. However, the value of $N_2O$ variation was almost zero.