• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.8
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• pp.1005-1012
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• 2021

Recently, the use of various location-based services-based location information systems using maps on the web has been expanding, and there is a need for a monitoring system that can check power demand in real time as an alternative to energy saving. In this study, we developed a deep learning real-time virtual power demand prediction web system using open source-based mapping service to analyze and predict the characteristics of power demand data using deep learning. In particular, the proposed system uses the LSTM(Long Short-Term Memory) deep learning model to enable power demand and predictive analysis locally, and provides visualization of analyzed information. Future proposed systems will not only be utilized to identify and analyze the supply and demand and forecast status of energy by region, but also apply to other industrial energies.

• Journal of Energy Engineering
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• v.23 no.4
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• pp.31-40
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• 2014

The Korean government made the 2nd Energy Basic Plan to achieve 11% of new and renewable energies distribution rate until 2035 as a response to cope with international discussion about greenhouse gas emission reduction. Renewable energies include solar thermal, photovoltaic, bioenergy, wind power, small hydropower, geothermal energy, ocean energy, and waste energy. New energies contain fuel cells, coal gasification and liquefaction, and hydrogen. As public and private investment to enhance the distribution of new and renewable energies, it is necessary to clarify the economic effects of the new and renewable energies sector. To the end, this study attempts to apply an input-output analysis and analyze the economic effects of new and renewable energies sector using 2012 input-output table. Three topics are dealt with. First, production-inducing effect, value-added creation effect, and employment-inducing effect are quantified based on demand-driven model. Second, supply shortage effects are analyzed employing supply-driven model. Lastly, price pervasive effects are investigated applying Leontief price model. The results of this analysis are as follows. First, one won of production or investment in new and renewable energies sector induces 2.1776 won of production and 0.7080 won of value-added. Moreover, the employment-inducing effect of one billion won of production or investment in new and renewable energies sector is estimated to be 9.0337 persons. Second, production shortage cost from one won of supply failure in new and renewable energies sector is calculated to be 1.6314 won, which is not small. Third, the impact of the 10% increase in new and renewable energies rate on the general price level is computed to be 0.0123%, which is small. This information can be utilized in forecasting the economic effects of new and renewable energies sector.

• Journal of Science Education
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• v.35 no.2
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• pp.204-220
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• 2011

This study was intended to develop an experimental module based on inquiry processes conducted by photosynthesis scientists. It was aimed to enhance scientific inquiry ability of the middle school students by applying the developed module. Developed module included some experiments conducted by earlier photosynthesis scientists such as Helmont, Woodward, Priestly, Hales and Ingen-Hausz. Inquiry process was involved in the developed module for instructing the inquiry methods. Rapid-cycling Brassica rapa known as a Fast Plant was used for the experimental material. Developed module was applied to the experimental group consisting 27 eighth grader, while experiments suggested in the science textbook was applied to the control group consisting 30 eighth grader. Developed module was more effective in improving students' scientific inquiry ability, especially measuring, forecasting and hypothesizing ability as its subordinate elements. When the result of post-test was compared to one of pre-test in the experimental group, their observing, forecasting, and generalization ability were improved. Experimental group showed that students' conception in photosynthesis and conceptual development related with the role of plants in the ecosystem and plant's food and movement of the water and nutrients were also improved. Before application, students in the experimental group did not have enough understanding of the abstract concept such as the existence or the role of the materials like $CO_2$ or $O_2$ or the energy accumulation. Developed module could help students to achieve the comprehensive concept regarding the role of plants as producers of organic matter and oxygen and to enhance their scientific inquiry ability and concepts regarding photosynthesis.

• Journal of the Korean Society of Hazard Mitigation
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• v.8 no.3
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• pp.23-27
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• 2008

The new conjunctive surface-subsurface flow model at a large scale was developed by using a 1-D Diffusion Wave (DW) model for surface flow interacting with the 3-D Volume Averaged Soil-moisture Transport (VAST) model for subsurface flow for the comprehensive terrestrial water and energy predictions in Land Surface Models (LSMs). A selection of numerical implementation schemes is employed for each flow component. The 3-D VAST model is implemented using a time splitting scheme applying an explicit method for lateral flow after a fully implicit method for vertical flow. The 1-D DW model is then solved by MacCormack finite difference scheme. This new conjunctive flow model is substituted for the existing 1-D hydrologic scheme in Common Land Model (CLM), one of the state-of-the-art LSMs. The new conjunctive flow model coupled to CLM is tested for a study domain around the Ohio Valley. The simulation results show that the interaction between surface flow and subsurface flow associated with the flow routing scheme matches the runoff prediction with the observations more closely in the new coupled CLM simulations. This improved terrestrial hydrologic module will be coupled to the Climate extension of the next-generation Weather Research and Forecasting (CWRF) model for advanced regional, continental, and global hydroclimatological studies and the prevention of disasters caused by climate changes.

• Journal of the Korean Institute of Gas
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• v.17 no.4
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• pp.58-69
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• 2013

This paper presents production data analysis for two production wells located in the shale gas field, Canada, with the proper analysis method according to each production performance characteristics. In the case A production well, the analysis was performed by applying both time and superposition time because the production history has high variation. Firstly, the flow regimes were classified with a log-log plot, and as a result, only the transient flow was appeared. Then the area of simulated reservoir volume (SRV) analyzed based on flowing material balance plot was calculated to 180 acres of time, and 240 acres of superposition time. And the original gas in place (OGIP) also was estimated to 15, 20 Bscf, respectively. However, as the area of SRV was not analyzed with the boundary dominated flow data, it was regarded as the minimum one. Therefore, the production forecasting was conducted according to variation of b exponent and the area of SRV. As a result, estimated ultimate recovery (EUR) increased 1.2 and 1.4 times respectively depending on b exponent, which was 0.5 and 1. In addition, as the area of SRV increased from 240 to 360 acres, EUR increased 1.3 times. In the case B production well, the formation compressibility and permeability depending on the overburden were applied to the analysis of the overpressured reservoir. In comparison of the case that applied geomechanical factors and the case that did not, the area of SRV was increased 1.4 times, OGIP was increased 1.5 times respectively. As a result of analysis, the prediction of future productivity including OGIP and EUR may be quite different depending on the analysis method. Thus, it was found that proper analysis methods, such as pseudo-time, superposition time, geomechanical factors, need to be applied depending on the production data to gain accurate results.

• Journal of Energy Engineering
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• v.28 no.4
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• pp.82-93
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• 2019

In this study, a new model is proposed to improve the problem of the decline of predict rate of heat demand on a particular date, such as a public holiday for the conventional heat demand forecasting system. The proposed model was the Four Season Mixed Heat Demand Prediction Neural Network Model, which showed an increase in the forecast rate of heat demand, especially for each type of forecast date (weekday/weekend/holiday). The proposed model was selected through the following process. A model with an even error for each type of forecast date in a particular season is selected to form the entire forecast model. To avoid shortening learning time and excessive learning, after each of the four different models that were structurally simplified were learning and a model that showed optimal prediction error was selected through various combinations. The output of the model is the hourly 24-hour heat demand at the forecast date and the total is the daily total heat demand. These forecasts enable efficient heat supply planning and allow the selection and utilization of output values according to their purpose. For daily heat demand forecasts for the proposed model, the overall MAPE improved from 5.3~6.1% for individual models to 5.2% and the forecast for holiday heat demand greatly improved from 4.9~7.9% to 2.9%. The data in this study utilized 34 months of heat demand data from a specific apartment complex provided by the Korea District Heating Corp. (January 2015 to October 2017).

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.4
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• pp.420-425
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• 1999

The maximum demand controller is an electrical equipment installed at the consumer side of power system for monitoring the electrical energy consumed during every integrating period and preventing the target maximum demand (MD) being exceeded by disconnecting sheddable loads. By avoiding the peak loads and spreading the energy requirement the controller contributes to maximizing the utility factor of the generator systems. It results in not only saving the energy but also reducing the budget for constructing the natural base facilities by keeping thc number of generating plants ~ninimumT. he conventional MD controllers often bring about the large number of control actions during the every inteyating period and/or undesirable loaddisconnecting operations during the beginning stage of the integrating period. These make the users aviod the MD controllers. In this paper. fuzzy control technique is used to get around the disadvantages of the conventional MD control system. The proposed MD controller consists of the predictor module and the fuzzy MD control module. The proposed forecasting method uses the SOFM neural network model, differently from time series analysis, and thus it has inherent advantages of neural network such as parallel processing, generalization and robustness. The MD fuzzy controller determines the sensitivity of control action based on the time closed to the end of the integrating period and the urgency of the load interrupting action along the predicted demand reaching the target. The experimental results show that the proposed method has more accurate forecastinglcontrol performance than the previous methods.

• Journal of the Korean earth science society
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• v.43 no.2
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• pp.265-282
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• 2022

This study analyzes the impact of long-range transport of biomass burning emissions from northeastern China on the concentration of particulate matter of diameter less than 10 ㎛ (PM₁₀) in Korea using the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). Korea was impacted by anthropogenic emissions from eastern China, dust storms from northern China and Mongolia, and biomass burning emissions from northeast China between April 4-and 7, 2020. The contributions of long-range PM₁₀ transport were calculated by separating biomass burning emissions from mixed air pollutants with anthropogenic emissions and dust storms using the zeroing-out method. Further, the radiation energy budget over land and sea around the Korean Peninsula was analyzed according to the distribution of biomass burning emissions. Based on the WRF-Chem simulation during April 5-6, 2020, the contribution of long-range transport of biomass burning emissions was calculated as 60% of the daily PM₁₀ average in Korea. The net heat flux around the Korean Peninsula was in a negative phase due to the influence of the large-scale biomass burning emissions. However, the contribution of biomass burning emissions was analyzed to be <45% during April 7-8, 2020, when the anthropogenic emissions from eastern China were added to biomass burning emissions, and PM₁₀ concentration increased compared with the concentration recorded during April 5-6, 2020 in Korea. Furthermore, the net heat flux around the Korean Peninsula increased to a positive phase with the decreasing influence of biomass burning emissions.

• Journal of Forest and Environmental Science
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• v.28 no.4
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• pp.205-211
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• 2012

Yield tables are a frequently used data base for regional timber resource forecasting. A normal yield table is based on two independent variables, age and site (species constant), and applies to fully stocked (or normal) stands while empirical yield tables are based on average rather than fully stocked stands. Normal and empirical yield tables essentially have many limitations. The limitations of normal and empirical yield tables led to the development of variable density yield tables. Mathematical models for estimating timber yields are usually developed by fitting a suitable equation to observed data. The model is then used to predict yields for conditions resembling those of the original data set. It may be accurate for the specific conditions, but of unproven accuracy or even entirely useless in other circumstances. Thus, these models tend to be specific rather than general and require validation before applying to other areas. Dalbergia sissoo forms a major portion of irrigated plantations in the hot desert of India and is an important timber tree species where stem wood is primarily used as timber. Variable density yield model is not available for this species which is very crucial in long-term planning for managing the plantations on a sustained basis. Thus, the objective of this study was to develop variable density yield model based on the data collected from 30 sample plots of D. sissoo laid out in IGNP area of Rajasthan State (India) and measured annually for 5 years. The best approximating model was selected based on the fit statistics among the models tested in the study. The model develop was evaluated based on quantitative and qualitative statistical criteria which showed that the model is statistically sound in prediction. The model can be safely applied on D. sissooo plantations in the study area or areas having similar conditions.

• Journal of environmental and Sanitary engineering
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• v.8 no.2
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• pp.25-48
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• 1993

International concern over the environmental pollution is ever increasing, and diversified countermeasures must be devised in Korea also. Global trend, damages, problems and countermeasures with respect to issues mentioned in the Rio Declaration, such as prevention of ozone layer destruction, reduction of migratory atmospheric pollution between neighboring countries, and prevention of global greenhouse effect, were discussed in this report. Conclusion of the report is summarized as follows : A. Measurement, Planning and Monitoring (1) Development and implementation of a global network for measurement and monitoring from the global aspects such factors as related to acid rain(Pioneer substances, pH, sulfate, nitrate), effect of global temperature(Air temperature, $CO_2$, $CH_4$, CFC, $N_2O$) and destruction of ozone layer($CFC_S$). (2) Establishment of network system via satellite monitoring movement of regional air mass, damage on the ozone layer and ground temperature distribution. B. Elucidation of Present State (1) Improvement and development of devices for carbon circulation capable of accurately forecasting input and output of carbon. (2) Developmental research on chemical reactions of greenhouse gas in the air. (3) Improvement and development of global circulation model(GCM) C. Impact Assessment Impact assessment on ecosystem, human body, agriculture, floodgate, land use, coastal ecology, industries, etc. D. Preventive Measures and Technology Development (1) Development and consumption of new energy (2) Development of new technology for removal of pioneer substances (3) Development of substitute matter for $CFC_S$ (4) Improvement of agriculture and forestry means to prevent the destruction of ozone layer and the greenhouse effect of the globe (5) Improvement of housing to prevent the destruction of ozone layer and the greenhouse effect of the globe (6) Development of new technology for probing underground water (7) Preservation of forest (8) Biomass 5. Policy Development (1) Development of strategy model (2) Development of long term forecast model (3) Development of penalty charge effect and expense evaluation methods (4) Feasibility study on regulations By establishing the above mentioned measures for environmentally sound and sustainable development to establish the right to live for humankind and to preserve the one and only earth.