• Journal of the Korea Convergence Society
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• v.8 no.1
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• pp.19-23
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• 2017

Energy is a vital resource for the economic growth and the social development for any country. As the industry becomes more sophisticated and the economy more grows, the electricity demand is increasing. So forecasting electricity demand is an important for electricity suppliers. Forecasting electricity demand makes it possible to distribute electricity demand. As the market for Negawatt market began to grow in Korea from 2014, the prediction of electricity consumption demand becomes more important. Moreover, power consumption forecasting provides a way for demand management to be directly or indirectly participated by consumers in the electricity market. We use Genetic Algorithms to predict the energy demand of the fishing industry in Jeju Island by using GDP, per capita gross national income, value add, and domestic electricity consumption from 1999 to 2011. Genetic Algorithm is useful for finding optimal solutions in various fields. In this paper, genetic algorithm finds optimal parameters. The objective is to find the optimal value of the coefficients used to predict the electricity demand and to minimize the error rate between the predicted value and the actual power consumption values.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.421-424
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• 2013

최근 문제가 되고 있는 전력 문제를 효율적으로 관리하기 위해 건물 에너지 관리 시스템이 주목받고 있다. 건물 에너지 관리 시스템은 관리자가 건물의 전력 소비량을 효율적으로 관리할 수 있도록 전력 소비량에 대한 모니터링 기능을 제공하는 시스템이다. 기존의 건물 에너지 관리 시스템은 과거, 현재, 미래의 전력 소비량을 통계 자료로 제공하고, 이를 토대로 전력 과부하 발생을 방지하였다. 그렇지만 기존의 시스템에 반응형 웹 디자인을 적용한 사례를 찾아보기 힘들며 온도 변화에 따른 전력 소비량을 고려하지 않기 때문에 정확한 부하 예측을 하기 어렵다는 단점이 있다. 본 논문에서 제안한 건물 에너지 관리 시스템은 반응형 웹 디자인을 적용하여 여러 모바일 기기로도 편리하고 효율적으로 건물을 관리할 수 있게 하였다. 또한, 건물에서 유지되어야 할 목표 온도, 건물 전력 소비량에 대한 과거 데이터와 기상청에서 제공하는 데이터를 통하여 부하 예측을 하고, 다양한 전력 소비량 통계 자료를 제공한다. 이를 통해 관리자는 효율적인 건물 에너지 관리를 할 수 있다.

• Journal of IKEEE
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• v.22 no.3
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• pp.822-828
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• 2018

Recently, energy issues such as massive blackout due to increase in power consumption have been emerged, and it is necessary to improve the accuracy of prediction of power consumption as a solution for these problems. In this study, we investigate the difference between the actual power consumption and the predicted power consumption through the deep learning- based power consumption forecasting experiment, and the possibility of adjusting the power reserve ratio. In this paper, the prediction of the power consumption based on the deep learning can be used as a basis to reduce the power reserve ratio so as not to excessively produce extra power. The deep learning method used in this paper uses a learning model of long-short-term-memory (LSTM) structure that processes time series data. In the computer simulation, the generated power consumption data was learned, and the power consumption was predicted based on the learned model. We calculate the error between the actual and predicted power consumption amount, resulting in an error rate of 21.37%. Considering the recent power reserve ratio of 45.9%, it is possible to reduce the reserve ratio by 20% when applying the power consumption prediction algorithm proposed in this study.

• Journal of the Society of Disaster Information
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• v.18 no.3
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• pp.532-541
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• 2022

Purpose: The purpose of this study is to suggest that the existing power demand prediction method including power demand according to fine dust is included in the existing power consumption by using an air purifier to improve the air quality due to fine dust. Method: The method of the study was compared and analyzed using data on the concentration of fine dust in Seoul for three years, household power consumption, and climate observation, and the effect of fine dust on power consumption in Seoul was identified in April and October. Result: The power consumption of home air purifiers in Seoul due to fine dust differences between April and October was calculated to be 2,141 MWh, accounting for 3.4% of the total difference in the use of home appliances in April and October. Conclusion: The effect of fine dust on household power consumption was verified, and power demand prediction is essential for economic system operation and stable power supply, so power consumption due to fine dust should be considered as well as focusing on power consumption of existing air conditioners and heaters.

• Smart Media Journal
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• v.12 no.11
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• pp.18-26
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• 2023

The building energy management system (BEMS), a system designed to efficiently manage energy production and consumption, aims to address the variable nature of power consumption within buildings due to their physical characteristics, necessitating stable power supply. In this context, accurate prediction of building energy consumption becomes crucial for ensuring reliable power delivery. Recent research has explored various approaches, including time series analysis, statistical analysis, and artificial intelligence, to predict power consumption. This paper analyzes the strengths and weaknesses of the Prophet model, choosing to utilize its advantages such as growth, seasonality, and holiday patterns, while also addressing its limitations related to data complexity and external variables like climatic data. To overcome these challenges, the paper proposes an algorithm that combines the Prophet model's strengths with the gated recurrent unit (GRU) to forecast short-term (2 days) and medium-term (7 days, 15 days, 30 days) building energy consumption. Experimental results demonstrate the superior performance of the proposed approach compared to conventional GRU and Prophet models.

• Bulletin of the Korean Space Science Society
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• 2003.10a
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• pp.72-72
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• 2003

위성체 열설계의 기본 목적은 가혹한 우주 열환경 하에서 위성체를 보호하며, 위성이 임무를 보호하며, 위성이 임무를 수행하는 동안에 어떠한 우주 열환경 하에서도 모든 위성 부품이 허용되는 온도 내에서 작동하도록 하는 것이다. 발사시 열해석은 궤도상에서의 열해석과 달리 초기 조건인 발사시간을 기준으로 열해석을 수행하게 된다. 열해석에서는 위성체가 발사체에 탑재되기까지의 과정과 발사 후에 발사체와 분리되는 시점까지 고려하게 된다. 위성체의 형상은 태양전지판이 접혀있으며, 배터리만이 위성체에 전력을 공급하는 역할을 하게 된다. 발사시에 전력소비량을 감소시키는 유일한 방법은 히터소비량을 줄이는 것이며, 이 점에서 발사시 열해석이 중요해진다. 본 연구에서는 저궤도 위성 발사시에 최대 히터소비량을 예측하기 위하여 저온 조건을 가정하고 열모델을 작성하고 열해석을 수행하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.720-722
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• 2009

Optimal operation of electric power generating plants is very essential for any power utility organization to reduce input costs and possibly the prices of electricity in general. This paper developed models for load forecasting using neural networks approach. This model is tested using actual load data of the Busan and weather data to predict the load of the Busan for one month in advance. The test results showed that the neural network forecasting approach is more suitable and efficient for a forecasting application.

• 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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• 2010.05a
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• pp.886-889
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• 2010

Power consumption measurement in sensor network is difficult to proceed by survey in real field. Thus, through simulation, the power consumption is estimated and replacement time of nodes are decided. A simulation tool simulates various facts such as power consumption, packet transmission traffic, network topology and etc. In this paper, it suggests sensor node power model to simulate power consumption which has large importance among simulation facts in sensor network. This model omits calculating expressions that the data originally surveyed can substitute with, according to power consumption property of each functions in sensor node in order to minimize calculations in simulation. In this case accuracy of power consumption estimation will be reduced, but can simulate it faster due to reduced calculation. Suggested model is fitted to analyze power consumption difference between two or more sensor network algorithms with rapid simulation speed rather than accurate simulation.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.43 no.1 s.343
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• pp.57-70
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• 2006

This paper descWireless Sensor Network consisting of a number of small sensors with transceiver and data processor is an effective means for gathering data in a variety of environments. The data collected by each sensor is transmitted to a processing center that use all reported data to estimate characteristics of the environment or detect an event. This process must be designed to conserve the limited energy resources of the sensor since neighboring sensors generally have the data of similar information. Therefore, clustering scheme which sends aggregated information to the processing center may save energy. Existing multi-hop cluster energy consumption modeling scheme can not estimate exact energy consumption of an individual sensor. In this paper, we propose a new cluster energy consumption model which modified existing problem. We can estimate more accurate total energy consumption according to the number of clusterheads by using Voronoi tessellation. Thus, we can realize an energy efficient cluster formation. Our modeling has an accuracy over $90\%$ when compared with simulation and has considerably superior than existing modeling scheme about $60\%.$ We also confirmed that energy consumption of the proposed modeling scheme is more accurate when the sensor density is increased.