• Communications for Statistical Applications and Methods
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• v.29 no.6
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• pp.709-719
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• 2022

Recently, as the importance of environmental protection has emerged, interest in new and renewable energy is also increasing worldwide. In particular, the solar energy sector accounts for the highest production rate among new and renewable energy in Korea due to its infinite resources, easy installation and maintenance, and eco-friendly characteristics such as low noise emission levels and less pollutants during power generation. However, although climate prediction is essential since solar power is affected by weather and climate change, solar radiation, which is closely related to solar power, is not currently forecasted by the Korea Meteorological Administration. Solar radiation prediction can be the basis for establishing a reasonable new and renewable energy operation plan, and it is very important because it can be used not only in solar power but also in other fields such as power consumption prediction. Therefore, this study was conducted for the purpose of improving the accuracy of solar radiation. Solar radiation was predicted by a total of three weather variables, temperature, humidity, and cloudiness, and solar radiation outside the atmosphere, and the results were compared using various models. The CatBoost model was best obtained by fitting and comparing the Boosting series (XGB, CatBoost) and RNN series (Simple RNN, LSTM, GRU) models. In addition, the results were further improved through Time series cross-validation.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.9
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• pp.47-54
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• 2009

These clays, because of world economy recession, exports decreased rapidly and manufacturing industry growth fell into negative. Industrial power consumption has been reduced about 7[%] that forms 53[%] of total load demand in Korea. And also, daily load pattern has been changed in several ways because of power consumption decrease influenced by domestic demand recession and heating power load decreased by the rise in temperature. This research analyzes, by analyzing maximum load demand, average load demand, load pattern based on relative factor, and load sensitiveness in accordance with temperature, that maximum load demand is more sensitive to atmospheric temperature than GDP growth rate and average load demand tends to be reduced according to GDP growth rate. I suppose KPX could operate the network system economically and safely by forecasting load demand in winter and summer seasons based on the results.

• Journal of IKEEE
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• v.23 no.1
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• pp.120-126
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• 2019

Recently, accurate prediction of power consumption based on machine learning techniques in Internet of Energy (IoE) has been actively studied using the large amount of electricity data acquired from advanced metering infrastructure (AMI). In this paper, we propose a deep learning model based on Gated Recurrent Unit (GRU) as an artificial intelligence (AI) network that can effectively perform pattern recognition of time series data such as the power consumption, and analyze performance of the prediction based on real household power usage data. In the performance analysis, performance comparison between the proposed GRU-based learning model and the conventional learning model of Long Short Term Memory (LSTM) is described. In the simulation results, mean squared error (MSE), mean absolute error (MAE), forecast skill score, normalized root mean square error (RMSE), and normalized mean bias error (NMBE) are used as performance evaluation indexes, and we confirm that the performance of the prediction of the proposed GRU-based learning model is greatly improved.

• Journal of IKEEE
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• v.23 no.1
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• pp.134-142
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• 2019

As the development of internet of energy (IoE) technologies and spread of various electronic devices have diversified patterns of energy consumption, the reliability of demand prediction has decreased, causing problems in optimization of power generation and stabilization of power supply. In this study, we propose a deep learning method, 1-Dimention-Convolution and Bidirectional Long Short-Term Memory (1D-ConvBLSTM), that combines a convolution neural network (CNN) and a Bidirectional Long Short-Term Memory(BLSTM) for highly reliable demand forecasting by effectively extracting the energy consumption pattern. In experimental results, the demand is predicted with the proposed deep learning method for various number of learning iterations and feature maps, and it is verified that the test data is predicted with a small number of iterations.

• Proceedings of the Korea Information Processing Society Conference
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• 2016.04a
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• pp.571-574
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• 2016

최근 에너지 효율을 최적화하는 차세대 지능형 전력망인 스마트 그리드 시스템(Smart Grid System)이 국내외에 널리 보급되고 있다. 그로 인해 그리드 시스템의 효율적인 운영을 위해 적용되는 EMS(Energy Management System) 기술의 중요성이 커지고 있다. EMS는 에너지 사용량 예측의 높은 정확성이 요구되며, 예측이 정확하게 수행될수록 에너지의 활용성이 높아진다. 본 논문은 전력 사용량 예측의 정확성 향상을 위한 새로운 기법을 제안한다. 구체적으로, 먼저 사용량에 영향을 미치는 환경적인 요인들을 분석한다. 분석된 요인들을 적용하여 유사한 환경을 가지는 전력 사용량 데이터의 사전 군집화를 수행한다. 그리고 예측 일에 관련된 환경 정보와 가장 유사한 군집의 전력 사용량 데이터를 기반으로 전력 사용량을 예측한다. 제안하는 기법의 성능을 평가하기 위해, 다양한 실험을 통하여 일간 전력 사용량을 예측하고 그 정확성을 측정하였다. 결과적으로, 기존의 기법들과 비교했을 때, 최대 52.88% 향상된 전력 사용량 예측 정확성을 보였다.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.5
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• pp.458-465
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• 2020

The purpose of this study is to analyze the effect of policy in Jeju, using a macro-economic forecasting model of Jeju. First, the model's reality explanatory power improved by updating its statistics to 2017 and expanding new policy variables and modules. Also, the industrial structure of the model was further subdivided and extended to be considered simultaneously in the demand side of Keynesian theory. Second, it was determined that the predictive power for the model of this study was better than that of the existing model. However, with some endogenous variables, it was possible to identify implications that should be developed and considered when the model is improved with additional data in the future. Third, when the second airport construction was considered, it was observed that its effect was an increase of 1.25 times for GRDP, 1.2 times for employment, 1.48 times for private consumption, and 2.06 times for investment. Also, the economic growth rate was estimated to be 1.6% point higher than when the second airport was not constructed. Finally, the results of this study are expected to be used for policy decision making of the Jeju Government.

• Economic Analysis
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• v.27 no.4
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• pp.1-42
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• 2021

The outlook for Korea's consumer price inflation rate has a profound impact not only on the Bank of Korea's operation of the inflation target system but also on the overall economy, including the bond market and private consumption and investment. This study presents the prediction results of consumer price inflation in Korea for the next three years. To this end, first, model selection is performed based on the out-of-sample predictive power of autoregressive distributed lag (ADL) models, AR models, small-scale vector autoregressive (VAR) models, and large-scale VAR models. Since there are many potential predictors of inflation, a Bayesian variable selection technique was introduced for 12 macro variables, and a precise tuning process was performed to improve predictive power. In the case of the VAR model, the Minnesota prior distribution was applied to solve the dimensional curse problem. Looking at the results of long-term and short-term out-of-sample predictions for the last five years, the ADL model was generally superior to other competing models in both point and distribution prediction. As a result of forecasting through the combination of predictions from the above models, the inflation rate is expected to maintain the current level of around 2% until the second half of 2022, and is expected to drop to around 1% from the first half of 2023.

• Journal of the Korea Society for Simulation
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• v.30 no.4
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• pp.1-8
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• 2021

Deep learning is one of the most widely accepted methods for the forecasting of time series data which have the complexity and non-linear behavior. In this paper, we investigate the modification of a state-of-art WaveNet deep learning architecture and walk forward validation (WFV) in order to forecast electric power consumption data 24-hour-ahead. WaveNet originally designed for raw audio uses 1D dilated causal convolution for long-term information. First of all, we propose a modified version of WaveNet which activates real numbers instead of coded integers. Second, this paper provides with the training process with tuning of major hyper-parameters (i.e., input length, batch size, number of WaveNet blocks, dilation rates, and learning rate scheduler). Finally, performance evaluation results show that the prediction methodology based on WFV performs better than on the traditional holdout validation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.629-631
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• 2015

Recently, the actual demand for electricity usage to out of demand forecasting demand appears to be based on the power of Government to address the insecurity is there are a lot of efforts on a more efficient energy management. In 2011, the first major outage, blackout since the current rate of no more than 10% of our power plants, such as power supply and demand crisis is being repeated. In addition, energy management systems, the demand for care and social areas are being expanded. In this paper, Building power supply and wired/wireless router and to optimize the DDNS (Dynamic Domain Name Service) for remote control and monitoring device for electric consumption Presonal Energy Management System offers a way to implement it. In the future, remote control and access the user's can minimize the settings for additional research is needed.

• Korean Journal of Agricultural Science
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• v.49 no.3
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• pp.679-690
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• 2022

As the supply and demand of pork has become a significant concern in Korea, controlling it has become a critical challenge for the industry. However, compared to the demand for pork, which has relatively stable consumption, it is not easy to maintain a stable supply. As the preparation of measures for a supply-demand crisis response and supply control in the pig industry has emerged as an important task, it has become necessary to establish a stable supply model and create an appropriate manual. In this study, a pork supply prediction model is constructed using reported data from the pig traceability system. Based on the derived results, a method for determining the supply-demand crisis stage using a statistical approach was proposed. From the results of the analysis, working days, African swine fever, heat wave, and Covid-19 were shown to affect the number of pigs graded in the market. A test of the performance of the model showed that both in-sample error rate and out-sample error rate were between 0.3 - 7.6%, indicating a high level of predictive power. Applying the forecast, the distribution of the confidence interval of the predicted value was established, and the supply crisis stage was identified, evaluating supply-demand conditions.