• New & Renewable Energy
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• v.18 no.2
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• pp.18-25
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• 2022

In this study, we propose a wind power generation prediction system that applies machine learning and data mining to predict wind power generation. This system increases the utilization rate of new and renewable energy sources. For time-series data, the data set was established by measuring wind speed, wind generation, and environmental factors influencing the wind speed. The data set was pre-processed so that it could be applied appropriately to the model. The prediction system applied the CNN (Convolutional Neural Network) to the data mining process and then used the LSTM (Long Short-Term Memory) to learn and make predictions. The preciseness of the proposed system is verified by comparing the prediction data with the actual data, according to the presence or absence of data mining in the model of the prediction system.

• Atmosphere
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• v.34 no.2
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• pp.177-185
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• 2024

As increasing global interest in renewable energy due to the ongoing climate crisis, there is a growing need for efficient technologies to manage such resources. This study focuses on the predictive skill of daily solar power generation using weather observation and forecast data. Meteorological data from the Korea Meteorological Administration and solar power generation data from the Korea Power Exchange were utilized for the period from January 2017 to May 2023, considering both inland (Daejeon) and coastal (Busan) regions. Temperature, wind speed, relative humidity, and precipitation were selected as relevant meteorological variables for solar power prediction. All data was preprocessed by removing their systematic components to use only their residuals and the residual of solar data were further processed with weighted adjustments for homoscedasticity. Four models, MLR (Multiple Linear Regression), RF (Random Forest), DNN (Deep Neural Network), and RNN (Recurrent Neural Network), were employed for solar power prediction and their performances were evaluated based on predicted values utilizing observed meteorological data (used as a reference), 1-day-ahead forecast data (referred to as fore1), and 2-day-ahead forecast data (fore2). DNN-based prediction model exhibits superior performance in both regions, with RNN performing the least effectively. However, MLR and RF demonstrate competitive performance comparable to DNN. The disparities in the performance of the four different models are less pronounced than anticipated, underscoring the pivotal role of fitting models using residuals. This emphasizes that the utilized preprocessing approach, specifically leveraging residuals, is poised to play a crucial role in the future of solar power generation forecasting.

• Journal of Digital Contents Society
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• v.17 no.3
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• pp.157-164
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• 2016

Solar power generation influenced by the weather. Using the weather forecast information, it is possible to predict the short-term solar power generation in the future. However, in limited circumstances such as islands or mountains, it can not be use weather forecast information by the disconnection of the network, it is impossible to use solar power generation prediction model using weather forecast. Therefore, in this paper, we propose a system that can predict the short-term solar power generation by using the information that can be collected by the system itself. We developed a short-term prediction model using the prior information of temperature and power generation amount to improve the accuracy of the prediction. We showed the usefulness of proposed prediction model by applying to actual solar power generation data.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.244-244
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• 2023

Both the frequency and the magnitude of hydrometeorological extreme events such as severe floods and droughts are increasing. In order to prevent a damage from the climatic disaster, hydrological models are often simulated under various meteorological conditions. While performing the simulations, a synthetic data generated through time series models which maintains the key statistical characteristics of the sample data are widely applied. However, the synthetic data can easily maintains both the average and the variance of the sample data, but the quantile is not maintained well. In this study, we proposes a data generation method which maintains the quantile of the sample data well. The equations of the former maintenance of variance extension (MOVE) are expanded to maintain quantile rather than the average or the variance of the sample data. The equations are derived and the coefficients are determined based on the characteristics of the sample data that we aim to preserve. Monte Carlo simulation is utilized to assess the performance of the proposed data generation method. A time series data (data length of 500) is regarded as the sample data and selected randomly from the sample data to create the data set (data length of 30) for simulation. Data length of the selected data set is expanded from 30 to 500 by using the proposed method. Then, the average, the variance, and the quantile difference between the sample data, and the expanded data are evaluated with relative root mean square error for each simulation. As a result of the simulation, each equation which is designed to maintain the characteristic of data performs well. Moreover, expanded data can preserve the quantile of sample data more precisely than that those expanded through the conventional time series model.

• The KIPS Transactions:PartD
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• v.14D no.2
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• pp.191-202
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• 2007

In general, test data generation techniques require the specification of an entire program path for automated test data generation. This paper presents a new way for generating test data automatically een without specifying a program path completely. For the ends, this paper presents a technique for transforming a program under test into Alloy which is the first order relational logic and then producing test data via Alloy analyzer. The proposed method reduces the burden of selecting a program path and also makes it easy to generate test data according to various test adequacy criteria. This paper illustrates the proposed method through simple, but illustrative examples.

• Fashion & Textile Research Journal
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• v.24 no.1
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• pp.81-94
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• 2022

The purpose of this study is to analyze the trends of MZ generation's lifestyle-related research from 2010 to 2020. As a result of searching keywords such as MZ generation's and lifestyle using academic database search sites, a total of 218 cases were used as analysis data to conduct frequency and content analysis. First, research type was 74 dissertations(34.6%), 144 journals(65.4%). The study of MZ generation was relatively active in journals. Second, the current status of academic field was 85(39.7%) in the social field, followed by 66(30.8%) in the arts/physical education, 21(9.8%) in the complex studies, 16(7.5%) in education, 15(7.0%) in nature, 6(2.8%) in engineering, 4(1.9%) in humanities, 1(0.5%) in agriculture/marine. Third, the current status of MZ generation research topics is 54 social participations(25.3%), 35 fashion/beauty(16.3%), 31 social/organizational adaptations(14.5%), 25 cultural/leisure activities(11.7%), 24 design/development projects(11.2%), 21 economic/employment/job projects(9.8%), 11 educational/career/experiences(5.1%), 9 self-concepts(4.2%), 4 welfare services(1.9%). Fourth, the current status of MZ generation research methods was quantitative research(survey/experiment) 125(58.4%), qualitative research(depth interview/participant observation) 42(19.6%), theory/literature research 35(16.4%) and mixed research 12(5.6%). Fifth, the study on the lifestyle of the MZ generation was conducted in four cases, one in 2016, one in 2019, two in 2020. This study is meaningful in that it grasped the overall flow of data of information exchange that can share the research trends of the MZ generation and suggested the basic data on the direction of future research, the individual tendency, behavior, and lifestyle characteristics of the MZ generation.

• Proceedings of the KIEE Conference
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• 2006.04b
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• pp.365-368
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• 2006

The objective of this research is to investigate usage of 3KW photovoltaic-wind power hybrid generation system composed of 500W solar power generator and 400W wind power generator in a parallel circuit. In addition, solar radiation meter and wind monitor have been installed into each generation system to obtain the practical operating data that monitored in monthly, daily and hourly. These data that are independent to weather change and location would provide adequate generation output on average and cope with emergency situation in generation system In conclusion, based on this study, it could be considered for 3KW combined generation system to be gradually propagated to houses and small-size public facilities.

• Journal of the Korean Society of Industry Convergence
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• v.13 no.2
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• pp.69-75
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• 2010

This paper describes the design and implementation of hybrid sun tracking solar power generation system designed by combining astronomical data with optical tracking mechanism. The advantages of proposed power generation system are small amounts of calculation for tracking operations and enhancement of 40% of power generation at best. This system is able to track toward optimal position for maximum sun-lights under scattered lights due to clouds. The performance of implemented power generation system is confirmed by field experiments.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.6B
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• pp.399-406
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• 2012

The objective of this research is to develop greenhouse gas generation models and estimation method of their parameters for solid waste landfills. Two models obtained by differentiating the Modified Gompertz and Logistic models were employed to evaluate two parameters of a first-order decay model, methane generation potential ($L_0$) and methane generation rate constant (k). The parameters were determined by the statistical comparison of predicted gas generation rate data using the two models and actual landfill gas collection data. The values of r-square obtained from regression analysis between two data showed that one model by differentiating the Modified Gompetz was 0.92 and the other model by differentiating the Logistic was 0.94. From this result, the estimation methods showed that $L_0$ and k values can be determined by regression analysis if landfill gas collection data are available. Also, new models based on two models obtained by differentiating the Modified Gompertz and Logistic models were developed to predict greenhouse gas generation from solid waste landfills that actual landfill generation data could not be available. They showed better prediction than LandGEM model. Frequency distribution of the ratio of Qcs (LFG collection system) to Q (prediction value) was used to evaluate the accuracy of the models. The new models showed higher accuracy than LandGEM model. Thus, it is concluded that the models developed in this research are suitable for the prediction of greenhouse gas generation from solid waste landfills.

• Journal of the Society of Disaster Information
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• v.4 no.1
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• pp.86-100
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• 2008

It is needed to classify the kinds of construction and demolition(c&d) debris to 6 catagories of waste concrete, waste asphalt concrete, waste wood, scraps, combustible waste and incombustible waste in order to properly do a separate discharge and to estimate unit generation rate in construction site. Also, in this case, the unit treating cost for mixed wastes should be applied with the unit treating cost for combustible waste. The construction standard materials estimation data is used for basic data for estimating unit generation rate. The mixed wastes in this data should be classified to waste wood, combustible waste and incombustible waste, and their ratio is obtained by using the unit generation rate of Asia Pacific Environment and Management Institute and Seoul Metropolitan Development Institute. The waste amounts generated from newly-built construction can be obtained from multiplying the loss rate by the amount of materials used from construction standard estimation data. Also, those from dismantling construction can be obtained by subtracting waste amount generated during newly-built construction from total input amount of materials in newly-built construction. Those in two cases can be used in construction site. It can be used for estimating the amount generated and establishing the treating plan in the case of setting up the policy of waste management and doing the environment impact assessment.