• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.31 no.3
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• pp.182-187
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• 2018

In this study, 20.8% of a p-type Si bifacial solar cell was used to develop a photovoltaic (PV) module to obtain the maximum power under a limited installation area. The transparent back sheet material was replaced during fabrication with a white one, which is opaque in commercial products. This is very beneficial for the generation of more electricity, owing to the additional power generation via absorption of light from the rear side. A new model is suggested herein to predict the power of the bifacial PV module by considering the backside reflections from the roof and/or environment. This model considers not only the frontside reflection, but also the nonuniformity of the backside light sources. Theoretical predictions were compared to experimental data to prove the validity of this model, the error range for which ranged from 0.32% to 8.49%. Especially, under $700W/m^2$, the error rate was as low as 2.25%. This work could provide theoretical and experimental bases for application to a distributed and microgrid network.

• The Journal of Society for e-Business Studies
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• v.24 no.4
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• pp.119-133
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• 2019

Recently, the importance prediction of photovoltaic power (PV) is considered as an essential function for scheduling adjustments, deciding on storage size, and overall planning for stable operation of PV facility systems. In particular, since most of PV power is generated in peak time, PV power prediction in a peak time is required for the PV system operators that enable to maximize revenue and sustainable electricity quantity. Moreover, Prediction of the PV power output in peak time without meteorological information such as solar radiation, cloudiness, the temperature is considered a challenging problem because it has limitations that the PV power was predicted by using predicted uncertain meteorological information in a wide range of areas in previous studies. Therefore, this paper proposes the LSTM (Long-Short Term Memory) based the PV power prediction model only using the meteorological, seasonal, and the before the obtained PV power before peak time. In this paper, the experiment results based on the proposed model using the real-world data shows the superior performance, which showed a positive impact on improving the PV power in a peak time forecast performance targeted in this study.

• Journal of the Korean earth science society
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• v.39 no.1
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• pp.1-22
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• 2018

Eight different data sets are examined in order to gain insight into the surface heat flux traits of the East Asian marginal seas. In the case of solar radiation of the East Sea (Japan Sea), Coordinated Ocean-ice Reference Experiments ver. 2 (CORE2) and the Objectively Analyzed Air-Sea Fluxes (OAFlux) are similar to the observed data at meteorological stations. A combination is sought by averaging these as well as the Climate Forecast System Reanalysis (CFSR) and the National Centers for Environmental Prediction (NCEP)-1 data to acquire more accurate surface heat flux for the East Asian marginal seas. According to the Combination Data, the annual averages of net heat flux of the East Sea, Yellow Sea, and East China Sea are -61.84, -22.42, and $-97.54Wm^{-2}$, respectively. The Kuroshio area to the south of Japan and the southern East Sea were found to have the largest upward annual mean net heat flux during winter, at -460- -300 and at $-370--300Wm^{-2}$, respectively. The long-term fluctuation (1984-2004) of the net heat flux shows a trend of increasing transport of heat from the ocean into the atmosphere throughout the study area.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.4
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• pp.1419-1436
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• 2020

For an energy harvesting sensor network, when the network lifetime is not the only primary goal, maximizing the network performance under environmental energy harvesting becomes a more critical issue. However, clustering protocols that aim at providing maximum information throughput have not been thoroughly explored in Energy Harvesting Wireless Sensor Networks (EH-WSNs). In this paper, clustering protocols are studied for maximizing the data transmission in the whole network. Based on a long short-term memory (LSTM) energy predictor and node energy consumption and supplement models, an uneven clustering protocol is proposed where the cluster head selection and cluster size control are thoroughly designed for this purpose. Simulations and results verify that the proposed scheme can outperform some classic schemes by having more data packets received by the cluster heads (CHs) and the base station (BS) under these energy constraints. The outcomes of this paper also provide some insights for choosing clustering routing protocols in EH-WSNs, by exploiting the factors such as uneven clustering size, number of clusters, multiple CHs, multihop routing strategy, and energy supplementing period.

• Steel and Composite Structures
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• v.2 no.6
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• pp.441-460
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• 2002

In the design of bridges, it is important to consider the thermal stresses induced by the non-linear temperature distribution as well as the variation of effective temperature in the bridge deck. To cope with this, design temperature profiles are provided by design codes, which are normally based on extensive research work. This paper presents the results of a comprehensive investigation on the thermal behaviour of bridges in Hong Kong with special emphasis on composite bridges. The temperature distribution in bridges depends primarily on the solar radiation, ambient air temperature and wind speed in the vicinity. Apart from data of the meteorological factors, good estimates of the thermal properties of material and the film coefficients are necessary for the prediction of temperature distribution. The design temperature profiles for various types of composite bridge deck with bituminous surfacing and concrete slab of different thicknesses are proposed. The factors affecting the design effective temperature are also reviewed and suitable values for Hong Kong are proposed. Results are compared with recommendations of the current local code. The method facilitates the development of site-specific temperature profiles for code documents, and it can also be applied to create zoning maps for temperature loading for large countries where there are great climatic differences.

• Proceedings of the SAREK Conference
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• 2006.06a
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• pp.789-794
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• 2006

The potential impacts of climate change on heating and cooling energy demand were investigated by means of transient building energy simulations and hourly weather data scenarios for Inchon. Future trends for the 21 st century was assessed based oil climate change scenarios with 7 global climate models(GCMs), We constructed hourly weather data from monthly temperatures and total incident solar radiation ($W/m^2$) and then simulated heating and cooling load by Trnsys 16 for Inchon. For 2004-2080, the selected scenarios made by IPCC foresaw a $3.7-5.8^{\circ}C$rise in mean annual air temperature. In 2004-2080, the annual cooling load for a apartment with internal heat gains increased by 75-165% while the heating load fell by 52-71%. Our analysis showed widely varying shifts in future energy demand depending on the season. Heating costs will significantly decrease whereas more expensive electrical energy will be needed of air conditioning during the summer.

• Journal of Korea Multimedia Society
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• v.24 no.10
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• pp.1414-1424
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• 2021

In this paper, the importance of input factors of a DNN (Deep Neural Network) PM_2.5 forecasting model using LRP(Layer-wise Relevance Propagation) is analyzed, and forecasting performance is improved. Input factor importance analysis is performed by dividing the learning data into time and PM_2.5 concentration. As a result, in the low concentration patterns, the importance of weather factors such as temperature, atmospheric pressure, and solar radiation is high, and in the high concentration patterns, the importance of air quality factors such as PM_2.5, CO, and NO₂ is high. As a result of analysis by time, the importance of the measurement factors is high in the case of the forecast for the day, and the importance of the forecast factors increases in the forecast for tomorrow and the day after tomorrow. In addition, date, temperature, humidity, and atmospheric pressure all show high importance regardless of time and concentration. Based on the importance of these factors, the LRP_DNN prediction model is developed. As a result, the ACC(accuracy) and POD(probability of detection) are improved by up to 5%, and the FAR(false alarm rate) is improved by up to 9% compared to the previous DNN model.

• Proceedings of the KSRS Conference
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• 1999.11a
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• pp.107-114
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• 1999

The large-scale distribution of crops Is usually determined by climate. We present the results of a climate-crop prediction based on spatial bio-physical process model approach, implemented in a GIS (Geographic Information System) environment using several regional and global agriculture-environmental databases. The model utilizes daily climate data like temperature, rainfall, solar radiation being generated stocastically by in-built model weather generator to determine the daily biomass and finally the crop yield. Crops are characterized by their specific growing period requirements, photosynthesis, respiration properties and harvesting index properties. Temperature and radiation during the growing period controls the development of each crop. The model simulates geographic/spatial distribution of climate by which a crop-growing belt can also be determined. The model takes both irrigated and non-irrigated area crop productivity into account and the potential increase in productivity by the technical means like mechanization is not considered. All the management input given at the base year 1995 was kept same for the next twenty-year changes until 2015. The simulated distributions of crops under current climatic conditions coincide largely with the current agricultural or specific crop growing regions. Simulation with assumed weather generated derived climate change scenario illustrate changes in the agricultural potential. There are large regional differences in the response across the country. The north-south and east-west regions responded differently with projected climate changes with increased and decreased productivity depending upon the crops and scenarios separately. When water was limiting or facilitating as non-irrigated and irrigated area crop-production effects of temperature rise and higher $CO_2$ levels were different depending on the crops and accordingly their production. Rise in temperature led to yield reduction in case of maize and rice whereas a gain was observed for wheat crop, doubled $CO_2$ concentration enhanced yield for all crops and their several combinations behaved differently with increase or decrease in yields. Finally, with this spatial modeling approach we succeeded in quantifying the crop productivity which may bring regional disparities under the different climatic scenarios where one region may become better off and the other may go worse off.

• Atmosphere
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• v.27 no.3
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• pp.317-329
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• 2017

Ocean mixed layer (OML) depth affects diurnal cycle of sea surface temperature (SST) induced by change of solar radiation absorption and heat budget in ocean. The diurnal SST variation can lead to convection over the ocean, which can impact on localized precipitation both over coastal and inland. In this study, we investigate the OML characteristics affecting the diurnal cycle of SST for the Korean Peninsula and surrounding areas. To analyze OML characteristics, HYCOM oceanic mixed layer depth (MLD) and wind field at 10 m from ERA-interim during 2008~2016 are used. In the winter, MLD is deeply formed when the strong wind field is located on perpendicular to continental slope over deep seafloor areas. Besides, cooling SST-induced vertical mixing in OML is reinforced by dry cold air originated from Siberia. The OML in summer is shallowly distributed about 20 m. In order to estimate the impact of OML model in high resolution NWP model, four experimental simulations are performed. At this time, the prognostic scheme of skin SST is applied in NWP to simulate diurnal SST. The simulation results show that CNTL (off-OML) overestimates diurnal cycle of SST, while EXPs (on-OML) indicate similar results to observations. The prediction performance for precipitation of EXPs shows improvement compared with CNTL over coastal as well as inland. This results suggest that the application of the OML model in summer season can contribute to improving the prediction for performance of SST and precipitation over coastal area and inland.

• Journal of the Korean Institute of Landscape Architecture
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• v.34 no.1 s.114
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• pp.21-36
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• 2006

This study was undertaken to analyze the effects of pavement thermal properties and water retention characteristics on the surface temperature of the gray permeable cement concrete pavement during the summer. Following is a summary of major results. 1) The hourly surface temperature of pavement could be well predicted with a heat transfer model program that incorporated the input data of major meteorological variables including solar radiation, atmospheric temperature, dew point, wind velocity, cloudiness and the evaporation rate of the pavements predicted by the time domain reflectometry (TDR) method. 2) When the albedo was changed to 0.5 from an arbitrary starting condition of 0.3, holding other variables constant, the peak surface temperature of the pavement dropped by 11.5%. When heat capacity was changed to $2.5\;kJm^{-3}K^{-1}\;from\;1.5\;kJm^{-3}K^{-1}$, surface temperature dropped by 8.0%. When daily evaporation was changed to 1 mm from 2 mm, temperature dropped by 5.7%. When heat conductivity was changed to $2.5\;Wm^{-1}K^{-1}\;from\;1.5\;Wm^{-1}K^{-1}$, the peak surface temperature of the pavement fell by 1.2%. The peak pavement surface temperature under the arbitrary basic condition was $24.46^{\circ}C$ (12 a.m.). 3) It accordingly became evident that the pavement surface temperature can be most effectively lowered by using materials with a high albedo, a high heat capacity or a high evaporation at the pavement surface. The glare situation, however, is intensified by raising of the albedo, moreover if reflected light is absorbed into surrounding physical masses, it is changed into heat. It accordingly became evident that raising the heat capacity and the evaporative capacity may be the moot acceptable measures to improve the thermal characteristics of the pavement. 4) The sensitivity of the surface temperature to major meteorological variables was as follows. When the daily average temperature changed to $0^{\circ}C\;from\;15^{\circ}C$, holding all other variables constant, the peak surface temperature of the pavement decreased by 56.1 %. When the global solar radiation changed to $200\;Wm^{-2}\;from\;600\;Wm^{-2}$, the temperature of the pavement decreased by 23.4%. When the wind velocity changed to $8\;ms^{-1}\;from\;4\;ms^{-1}$, the temperature decreased by 1.4%. When the cloudiness level changed to 1.0 from 0.5, the peak surface temperature decreased by 0.7%. The peak pavement surface temperature under the arbitrary basic conditions was $24.46^{\circ}C$ (12 a.m.)