• Korean Journal of Construction Engineering and Management
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• v.16 no.2
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• pp.65-76
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• 2015

The Clean Development Mechanism (CDM) is the multi-lateral 'cap and trade' system endorsed by the Kyoto Protocol. CDM allows developed (Annex I) countries to buy CER credits from New and Renewable (NE) projects of non-Annex countries, to meet their carbon reduction requirements. This in effect subsidizes and promotes NE projects in developing countries, ultimately reducing global greenhouse gases (GHG). To be registered as a CDM project, the project must prove 'additionality,' which depends on numerous factors including the adopted technology, baseline methodology, emission reductions, and the project's internal rate of return. This makes it difficult to determine ex ante a project's acceptance as a CDM approved project, and entails sunk costs and even project cancellation to its project stakeholders. Focusing on hydro power projects and employing UNFCCC public data, this research developed a prediction model using logistic regression and CART to determine the likelihood of approval as a CDM project. The AUC for the logistic regression and CART model was 0.7674 and 0.7231 respectively, which proves the model's prediction accuracy. More importantly, results indicate that the emission reduction amount, MW per hour, investment/Emission as crucial variables, whereas the baseline methodology and technology types were insignificant. This demonstrates that at least for hydro power projects, the specific technology is not as important as the amount of emission reductions and relatively small scale projects and investment to carbon reduction ratios.

• Smart Media Journal
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• v.12 no.10
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• pp.63-70
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• 2023

Recently, energy consumption for heating costs, which is 35% of smart farm energy costs, has increased, requiring energy consumption efficiency, and the importance of new and renewable energy is increasing due to concerns about the realization of electricity bills. Renewable energy belongs to hydropower, wind, and solar power, of which solar energy is a power generation technology that converts it into electrical energy, and this technology has less impact on the environment and is simple to maintain. In this study, based on the greenhouse heat storage tank and heat pump data, the factors that affect the heat storage tank are selected and a heat storage tank supply temperature prediction model is developed. It is predicted using Long Short-Term Memory (LSTM), which is effective for time series data analysis and prediction, and XGBoost model, which is superior to other ensemble learning techniques. By predicting the temperature of the heat pump heat storage tank, energy consumption may be optimized and system operation may be optimized. In addition, we intend to link it to the smart farm energy integrated operation system, such as reducing heating and cooling costs and improving the energy independence of farmers due to the use of solar power. By managing the supply of waste heat energy through the platform and deriving the maximum heating load and energy values required for crop growth by season and time, an optimal energy management plan is derived based on this.

• Journal of the Korean earth science society
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• v.33 no.4
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• pp.309-319
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• 2012

In order to clarify the characteristics of satellite based sea wind data assimilations applied for the estimation of wind resources around the Korean peninsula, several numerical experiments were carried out using WRF. Satellite sea wind data used in this study are QuikSCAT from NASA and ASCAT from ESA. When the wind resources are estimated with data assimilation, its estimation accuracy is improved clearly. Since the band width is broad for QuikSCAT, statistical accuracy of the estimated wind resources with QuikSCAT assimilations is better than that with ASCAT assimilations. But the wind estimated around sub-satellite point matches better with of ASCAT compared to QuikSCAT assimilation. The impact of sea wind data assimilation on the prediction of wind resources lasts for 6 hours after data assimilation starts, therefore the data assimilation processes using both fine spatial and temporal resolutions of sea wind are needed to make a more useful wind resource map of the Korean Peninsula.

• The Korean Journal of Applied Statistics
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• v.30 no.5
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• pp.633-645
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• 2017

This study proposes a statistical procedure for analyzing container ship operation data that can help determine fuel consumption patterns. We first investigate the features that affect fuel consumption and develop the prediction model to find current fuel consumption. The ship data can be divided into two-type data. One set of operation data includes sea route, voyage information, longitudinal water speed, longitudinal ground speed, and wind, the other includes machinery data such as engine power, rpm, fuel consumption, temperature, and pressure. In this study, we separate the effects of external force on ships according to Beaufort Scale and apply a partial least squares regression to develop a prediction model.

• Composites Research
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• v.28 no.6
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• pp.340-347
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• 2015

As resin impregnates through the fiber preform in vacuum assisted resin transfer molding process, the volume of fibers is changed by expansion of fiber mat according to filling time. It causes not only the change in dimension but also the decrease of mechanical properties of the composite product. Moreover, it results in the economic loss by increase of the used amount of resin especially in the large product such as wind turbine blade. In this study, the ways to control fiber volume fraction were investigated by both the experimental and theoretical analyses on the expansion of fiber preform as the preform was impregnated by resin in the VARTM process. Two kinds of swelling stage were observed as flow front progressed, which was analyzed by comparing the experimental and simulation results. The process parameters are expected to be optimized by investigating the swelling behavior of fiber preform in the manufacturing process of the composite product.

• Journal of the Korean Solar Energy Society
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• v.39 no.6
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• pp.93-112
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• 2019

In this paper, a very new web-based software for renewable energy system (RES) design and economic evaluation was introduced. This solution would provide the precise RES estimation service including not only photovoltaic (PV), wind turbine (WT) and fuel cell (FC) individually but also energy storage system (ESS) as combined forms with PV or WT. The three reasons why we ought to develop it are: First, the standardized tool suitable to the domestic environment for estimating power generation from RES facilities and economic evaluation is required. Secondly, the standardized tool is needed to spread domestic RES supply policy and to promote the new industry in the micro-grid field. The last, the reliability of economic evaluation should be enhanced more for new facilities. To achieve those aims, the weather database of one hundred locations have established and the RES facility database has also constructed. For the energy management, mathematical models for PV, WT, ESS and FC were developed. As a final phase, the analytical process to evaluate economics has performed with field data verification.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.16 no.9
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• pp.6411-6418
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• 2015

To respond the climate change and environmental pollution, the studies on renewable energy policies are increasing. The renewable energy is a new growth engine technology represented by the green industry and green technology. At present, the investments for the renewable energy supply and technology development projects of three main strategy sectors such as sunlight, wind power and hydrogen fuel cell are implemented in our country, while they are still in the early stage, accordingly reducing those uncertainty for the research direction and investment fields is the most urgent issue among others. Thus, this study applied text mining method and multinominal topic model among the big data analysis methods on our country's newspaper articles concerning the renewable energy over the last 10 years, and then analyzed the core issues and global research trend, forecasting the renewable energy fields with the growth potential. It is predicted that these results of the study based on information and communication technology will be actively applied on the renewable energy fields.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.2
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• pp.158-164
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• 2018

Wind power forecast is critical for efficient power system operation. However, wind power has high forecasting errors due to uncertainty caused by the climate change. These forecasting errors can have an adverse impact on the power system operation. In order to mitigate the issues caused by the wind power forecasting error, wind power curtailment and energy storage system (ESS) can be introduced in the power system. These methods can affect the economics of wind power resources. Therefore, it is necessary to evaluate the economics of the methods for mitigating the wind power forecasting error. This paper attempts to analyze the economics of wind power curtailment and ESS operation for mitigating wind power forecasting error. Numerical simulation results are presented to show the economic impact of wind power curtailment and ESS operation.

• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.396-397
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• 2011

As moving away from fossil fuel makes rapid progress, new paradigm has arisen in the power industry area. Developing alternative energy source is progressing actively, the proportion of renewable energy in electricity production is expected to be increased. Because the output of wind farm depends on wind characteristic, minimizing the output fluctuation is a key to keep the power system controllable and stable. Various compensation scheme for stabilizing the output of wind farm has been developed. Considering some requirements such as reaction velocity, controllability, scalability and applicability, energy storage system is one of the effective methods for spreading of renewable energy. In this paper, method of compensating method with forecasting algorithm was simulated, and then the results was analyzed.

• Proceedings of the KIEE Conference
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• 2009.07a
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• pp.1098_1099
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• 2009

In this paper, we present the estimation of wind turbine power generation using Cascade Architectures of Fuzzy Neural Networks(CAFNN). The proposed model uses the wind speed average, the standard deviation and the past output power as input data. The CAFNN identification process uses a 10-min average wind speed with its standard deviation. The method for rule-based fuzzy modeling uses Gaussian membership function. It has three fuzzy variables with three modifiable parameters. The CAFNN's configuration has three Logic Processors(LP) that are constructed cascade architecture and an effective optimization method uses two-level genetic algorithm. First, The CAFNN is trained with one-day average input variables. Once the CAFNN has been trained, test data are used without any update. The main advantage of using CAFNN is having simple structure of system with many input variables. Therefore, The proposed CAFNN technique is useful to predict the wind turbine(WT) power effectively and hence that information will be helpful to decide the control strategy for the WT system operation and application.