• The Korean Journal of Applied Statistics
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• v.32 no.2
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• pp.319-330
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• 2019

Forecasting the electricity consumption through analyzing the past electricity consumption a advantageous for energy planing and policy. Machine learning is widely used as a method to predict electricity consumption. Among them, ensemble learning is a method to avoid the overfitting of models and reduce variance to improve prediction accuracy. However, ensemble learning applied to daily data shows the disadvantages of predicting a center value without showing a peak due to the characteristics of ensemble learning. In this study, we overcome the shortcomings of ensemble learning by considering the temperature trend. We compare nine models and propose a model using random forest with the linear trend of temperature.

• Journal of Korean Institute of Industrial Engineers
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• v.39 no.1
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• pp.30-45
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• 2013

Predicting monthly electricity price has been a significant factor of decision-making for plant resource management, fuel purchase plan, plans to plant, operating plan budget, and so on. In this paper, we propose a sophisticated prediction model in terms of the technique of modeling and the variety of the collected variables. The proposed model hybridizes the semi-supervised learning and the artificial neural network algorithms. The former is the most recent and a spotlighted algorithm in data mining and machine learning fields, and the latter is known as one of the well-established algorithms in the fields. Diverse economic/financial indexes such as the crude oil prices, LNG prices, exchange rates, composite indexes of representative global stock markets, etc. are collected and used for the semi-supervised learning which predicts the up-down movement of the price. Whereas various climatic indexes such as temperature, rainfall, sunlight, air pressure, etc, are used for the artificial neural network which predicts the real-values of the price. The resulting values are hybridized in the proposed model. The excellency of the model was empirically verified with the monthly data of electricity price provided by the Korea Energy Economics Institute.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.5
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• pp.215-219
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• 2021

In this paper, we propose a deep reinforcement learning algorithm-based bi-directional electricity negotiation scheme that adjusts and propose the price they want to exchange for negotiation over smart building and utility grid. By employing a deep Q network algorithm, which is a kind of deep reinforcement learning algorithm, the proposed scheme adjusts the price proposal of smart building and utility grid. From the simulation results, it can be verified that consensus on electricity price negotiation requires average of 43.78 negotiation process. The negotiation process under simulation settings and scenario can also be confirmed through the simulation results.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.3
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• pp.7-14
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• 2021

The emergence of the automated smart grid has become an essential device for responding to these problems and is bringing progress toward a smart grid-based society. Smart grid is a new paradigm that enables two-way communication between electricity suppliers and consumers. Smart grids have emerged due to engineers' initiatives to make the power grid more stable, reliable, efficient and safe. Smart grids create opportunities for electricity consumers to play a greater role in electricity use and motivate them to use electricity wisely and efficiently. Therefore, this study focuses on power demand management through machine learning. In relation to demand forecasting using machine learning, various machine learning models are currently introduced and applied, and a systematic approach is required. In particular, the GP learning model has advantages over other learning models in terms of general consumption prediction and data visualization, but is strongly influenced by data independence when it comes to prediction of smart meter data.

• Clean Technology
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• v.28 no.2
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• pp.138-146
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• 2022

Electricity has become a factor that dramatically affects the market economy. The day-ahead system marginal price determines electricity prices, and system marginal price forecasting is critical in maintaining energy management systems. There have been several studies using mathematics and machine learning models to forecast the system marginal price, but few studies have been conducted to develop, compare, and analyze various machine learning and deep learning models based on a data-driven framework. Therefore, in this study, different machine learning algorithms (i.e., autoregressive-based models such as the autoregressive integrated moving average model) and deep learning networks (i.e., recurrent neural network-based models such as the long short-term memory and gated recurrent unit model) are considered and integrated evaluation metrics including a forecasting test and information criteria are proposed to discern the optimal forecasting model. A case study of South Korea using long-term time-series system marginal price data from 2016 to 2021 was applied to the developed framework. The results of the study indicate that the autoregressive integrated moving average model (R-squared score: 0.97) and the gated recurrent unit model (R-squared score: 0.94) are appropriate for system marginal price forecasting. This study is expected to contribute significantly to energy management systems and the suggested framework can be explicitly applied for renewable energy networks.

• Journal of Korean Elementary Science Education
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• v.36 no.2
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• pp.163-179
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• 2017

This study is to examine whether elementary science textbooks in Korea and Singapore are applicable to flipped learning. By comparative study we sought to identifying appropriate features of science textbooks for learner-centered teaching. We analyzed text pages on the unit of 'Working of electricity' in Korean elementary science textbook for sixth grade and three chapters of 'Electric circuits, Using electricity, Conductors of electricity' in Singaporean elementary textbook, 'Science : My pals are here!'. We designed evaluating frameworks for science textbooks based on the four pillars of flipped learning. and applied it to 10 elementary teachers evaluate two textbooks. They evaluated textbooks with Likert Scale items and wrote detailed statements and exemplars about their choices. We analyzed the teachers' evaluative descriptions inductively and chose commonly mentioned characteristics. Based on the analysis, we got to the conclusion about specific features of two elementary science textbooks in terms of flexible environment, learning culture, intentional contents, and teachers' expertises. Implications for improving science textbooks towards flipped learning and learner-centered teaching through comparative study were discussed.

• Proceedings of the KIEE Conference
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• 2001.07a
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• pp.386-388
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• 2001

The change of the electricity charge from cost base to price base due to the introduction of the electricity market competition causes consumer to choose a variety of charge schemes and a portion of loads to be affected by this change. Besides, it is required the index that consolidate the price volatility experienced on the power exchange with gaming and strategic bidding by suppliers to increase profits. Therefore, in order to find a mathematical model of the sensitively-responding-to-price loads, the price-sensitive load model is needed. And the development of state-of-the-art technologies affects the electricity price, so the diffusion of high-efficient end-uses and these price affect load patterns. This paper shows the analysis on learning curves algorithms which is used to investigate the correlation of the end-uses' price and load patterns.

• Convergence Security Journal
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• v.7 no.2
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• pp.9-16
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• 2007

In today's society, the paradigm is converted to information where both this and knowledge become the source of value added. In order to answer societies' needs school education is subject to a large change, that is 'from supplier-centered learners to demand-centered learners'. This study is the design and implementation of multimedia CAI program for learning about electricity in the meddle school the mechanics curriculum with a focus in generating electricity. Based on multimedia, software, educational utilization of the multimedia, CAI, learning theory and form, both technology and multimedia elements related to curriculum are analyzed. This title is composed of object, contents, evaluation and related sites every chapter for students to study. Also, it is designed for students to take interest and participate in the learning process actively by combining lots of media such as characters, sound, images and animation. Specially it is composed in a way of repetition according to the student's level.

• Journal of Korean Elementary Science Education
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• v.20 no.2
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• pp.217-228
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• 2001

The purpose of this study was to investigate the effect of learning cycle model on the changes of electricity conceptions of elementary students. Four classes in forth grade of an elementary school in Busan were selected and two of them were served as experimental group and the others as control group. The experimental group were taught the unit of "Light an electric bulb" in elementary science textbook with teaching model based on teaming cycle and the control group with traditional teaching style. The instruction effects were analyzed through pre and post-test results using questionnaire on the electricity. The results of pre-test showed that there was not a significant difference between experimental group and control group at .05 level, so two groups could be regarded as homogeneous. The mean score of experimental group was significantly higher than that of control group on the post-test at .05 level. And within-group comparison revealed that both groups made improvement on the mean score and that the improvement of each group had significant difference at .05 level. Above results said that the teaching model based on learning cycle, which focuses on hands-on activity and considers each student as an active subject, was more effective than traditional teaching style in improving the formation of scientific conceptions on electricity.ectricity.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.53 no.4
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• pp.216-222
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• 2004

This research is that concern in mathematics education of Technical high school Department of Electricity in the 7th educational curriculum. we indicated problem compare 7th with 6th mathematics curriculum subject contents in Technical high school Department of Electricity. And examine major subject contents, analyzed contents of mathematics that must supplement and mathematics which use in major subject. Established contents of electricity mathematics education that need to major learning to satisfy target of technical high school technical education that is presented in the 7th training courses with this analysis. Also, we hope these results are into consideration when writing new mathematics text in Technical high school Department of Electricity.