The purpose of this study is to analyze the urban heat island ultimately by analyzing long-wave radiation which is the dominant factor of night minimum temperature formation. We observed during two months with four elements which is long and short wave radiation, temperature, relative humidity. And we analyzed the correlation between the four factors of long-wave radiation, temperature, cloud form, and cloud amount during the summer two months on the night time. Observations were carried out at two sites in Daegu and nearby. The results are as follows. (1) Long-wave radiation change per $1^{\circ}C$ in summer was larger than winter. (2) Long-wave radiation amount is affected by temperature change when the amount of cloud is small. (3) Low cloud was analyzed to have more influence on long-wave radiation than high cloud.

The Ln-least method is commonly used to estimate the Weibull parameters from the observed wind speed data. In previous studies, the bin method has been used to calculate the cumulative frequency distribution for the Ln-least method. The purpose of this study is to obtain better performance in the Ln-least method by applying probability plotting position(PPP) instead of the bin method. Two types of the wind speed data were used for the analysis. One was the observed wind speed data taken from three sites with different topographical conditions. The other was the virtual wind speed data which were statistically generated by a random variable with known Weibull parameters. Also, ten types of PPP formulas were applied which were Hazen, California, Weibull, Blom, Gringorten, Chegodayev, Cunnane, Tukey, Beard and Median. In addition, in order to suggest the most suitable PPP formula for estimating Weibull parameters, two accuracy tests, the root mean square error(RMSE) and $R^2$ tests, were performed. As a result, all of PPPs showed better performances than the bin method and the best PPP was the Hazen formula. In the RMSE test, compared with the bin method, the Hazen formula increased estimation performance by 38.2% for the observed wind speed data and by 37.0% for the virtual wind speed data. For the $R^2$ test, the Hazen formula improved the performance by 1.2% and 2.7%, respectively. In addition, the performance of the PPP depended on the frequency of low wind speeds and wind speed variability.

The purpose of this paper is to apply the numerical solution procedure to compute conduction time series factors (CTSF) for construction materials with large thermal capacities. After modifying the procedure in Ref. [9], it is applied to find the CTSF for the wall type 19 and the roof type 18 of ASHRAE. The response periods for one hr pulse load are longer than 24hrs for these wall and roof. The CTSF generated using modified procedure agree well with the values presented in the ASHRAE handbook. The modified procedure is a general procedure that can be applied to find CTSF for materials with complex structures. For the large thermal capacity materials, it should be checked whether thermal response period of the material is over 24hr or not. With suggested solution procedure, it is easy to check the validity of the CTSF based on 24hr period.

The heating performance of a solar thermal seasonal storage system applied to a glass greenhouse was analyzed numerically. For this study, the gardening 16th zucchini greenhouse of Jeollanam-do agricultural research & extension services was selected. And, the heating load of the glass greenhouse selected was 576 GJ. BTES (Borehole Thermal Energy Storage) was considered as a seasonal storage, which is relatively economical. The TRNSYS was used to predict and analyze the dynamic performance of the solar thermal system. Numerical simulation was performed by modeling the solar thermal seasonal storage system consisting of flat plate solar collector, BTES system, short-term storage tank, boiler, heat exchanger, pump, controller. As a result of the analysis, the energy of 928 GJ from the flat plate solar collector was stored into BTES system and 393 GJ of energy from BTES system was extracted during heating period, so that it was confirmed that the thermal efficiency of BTES system was 42% in 5th year. Also since the heat supplied from the auxiliary boiler was 87 GJ in 5th year, the total annual heating demand was confirmed to be mostly satisfied by the proposed system.

This study aims to analyze the performance of solar thermal system with heat pump for domestic hot water and heat supply. There are four types of system. Systems are categorized based on the existence of a heat pump and the ways of controlling the working fluid circulating from the collector. Working fluid is controlled by either temperature level (categorized as system 1 and 2) or sequential flow (system 3 and 4). Heat balance of the system, the solar fraction, hot water and heating supply rates, and performance of heat pump are analyzed using TRNSYS and TESS component programs. Technical specifications of the main facilities are as follow; the area of the collector to $25m^2$, the volumes of the main tank and the buffer tank to $0.5m^3$ and $0.8m^3$, respectively. Heating capacity of the heat pump in the heating mode is set to 30,000 kJ / hr. Hot water supply set 65 liters per person each day, total heat transfer coefficient of the building to 1,500 kJ / kg.K. Indoor temperature is kept steadily around $22^{\circ}C$. The results are as follows; 6 months average solar fraction of system 1 turns out to be 39%, which is 6.7% higher than system 2 without the heat pump, indicating a 25% increase of solar fraction compared to that of system 2. In addition, the solar fraction of system 1 is 2% higher than that of system 3. Hot water and heating supply rate of system 1 are 93% and 35%, respectively. Considering the heat balance of the system, higher heat efficiency, and solar fraction, as whole, it can be concluded that system 1 is the most suitable system for hot water and heat supply.

The heating performance of a solar thermal seasonal storage system applied to a 1,320 m2 glass greenhouse was analyzed numerically, and the economic feasibility depending upon the number of boreholes was evaluated. For this study, the gardening 16th and 19th zucchini greenhouse of Jeollanam-do agricultural research & extension services was selected. And the heating load of the glass greenhouse selected was 1,147 GJ. BTES(Borehole Thermal Energy Storage) was considered as a seasonal storage, which is relatively economical. The number of boreholes was selected from 25 to 150. The TRNSYS was used to predict and analyze the dynamic performance of the solar thermal system. Numerical simulation was performed by modelling the solar thermal seasonal storage system consisting of flat plate solar collector, BTES system, short-term storage tank, boiler, heat exchanger, pump and controller. As a result of the analysis, when the number of boreholes was from 25 to 50, the thermal efficiency of BTES system and the solar fraction was the highest. When the number of boreholes was from 25 to 50, it was analyzed that the payback period was from 5.2 years to 6.2 years. Therefore it was judged to be the number of boreholes of the proposed system was from 25 to 50, which is the most efficient and economical.

The most crucial point of reducing building energy is application of high performance envelope. The amount of heat exchange through window is highest in comparison of other envelopes so that heat exchange through window influence directly with building energy consumption. The window energy performance can be define with thermal, leakage and optical performance. In previous study we can confirmed that not only thermal performance but also optical performance are considered, 11% to 15% of building energy consumption can be reduced. Smart window system has potential of energy saving so that many industry field use smart window system including architectural area and these aspect causes smart window market continuous growth year by year. In this study, building energy consumption has been analyzed which consist of smart window that dynamically control optical states. The consideration of standard commercial building model for research, the reference medium size commercial building model of DOE (Department Of Energy, USA) has been used. The building energy simulation result of 4 axis in 8 regions in Korea shows 8% to 22% reduction of building energy consumption by application of smart window system.