• Title, Summary, Keyword: 전력 소비량 예측

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Forecasting of Electricity Demand for Fishing Industry Based on Genetic Algorithm approach (유전자 알고리즘에 기반한 수산업 전력 수요 예측에 관한 연구)

  • Kim, Heung-Soe;Lee, Sung-Geun
    • 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.

Applying Responsive Web Design to a Building Energy Management System (반응형 웹 디자인을 적용한 건물 에너지 관리 시스템)

  • Kim, Kyu Ri;Lee, Hyun Ju;Na, Hyung Seon;Jung, Hwa Young;Lee, Yong Kyu
    • Proceedings of the Korea Information Processing Society Conference
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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.

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Power Consumption Prediction Scheme Based on Deep Learning for Powerline Communication Systems (전력선통신 시스템을 위한 딥 러닝 기반 전력량 예측 기법)

  • Lee, Dong Gu;Kim, Soo Hyun;Jung, Ho Chul;Sun, Young Ghyu;Sim, Issac;Hwang, Yu Min;Kim, Jin Young
    • 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.

저궤도위성 발사시 저온조건에 대한 열해석

  • 현범석;김희경;최준민
    • Bulletin of the Korean Space Science Society
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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.

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The Study on Load Forecasting Using Artificial Intelligent Algorithm (지능형 알고리즘을 이용한 전력 소비량 예측에 관한 연구)

  • Lee, Jae-Hyun
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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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.

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Power Model of Sensor Node for Relative Comparison of Power Consumption in Mobile Sensor Network (모바일 센서 네트워크 라우팅 알고리즘 간의 전력 소비량 비교를 위한 센서 노드 전력 모델)

  • Kim, Min-Je;Kim, Chang-Joon;Jang, Kyung-Sik
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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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.

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Modeling of the Cluster-based Multi-hop Sensor Networks (클거스터 기반 다중 홉 센서 네트워크의 모델링 기법)

  • Choi Jin-Chul;Lee Chae-Woo
    • Journal of the Institute of Electronics Engineers of Korea TC
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    • v.43 no.1
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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.

Efficient Grid-Independent ESS Control System by Prediction of Energy Production Consumption (에너지 생산량 소비량 예측을 통한 효율적인 계통 독립형 ESS 제어 시스템)

  • Joo, Jong-Yul;Oh, Jae-Chul
    • The Journal of the Korea institute of electronic communication sciences
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    • v.14 no.1
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    • pp.155-160
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    • 2019
  • In this paper, we propose an efficient grid-independent ESS control system through the control of renewable energy and agricultural ICT by utilizing the prediction of energy production and consumption. The proposed system is an integrated management system that can perform maintenance and monitoring by visualizing the accurate phase and data of power system. It can automatically cope, collect, process, and control the data. Also, it can analyze the power generation of solar power generation, consumption pattern of installed facilities, and operation trend of facilities. Further, it can predict the consumption of energy production and present the optimal energy management method by using the OpenAPI of the Korea Meteorological Administration, thereby reducing unnecessary energy consumption and operating cost.

Performance Comparison of Machine Learning in the Prediction for Amount of Power Market (전력 거래량 예측에서의 머신 러닝 성능 비교)

  • Choi, Jeong-Gon
    • The Journal of the Korea institute of electronic communication sciences
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    • v.14 no.5
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    • pp.943-950
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    • 2019
  • Machine learning can greatly improve the efficiency of work by replacing people. In particular, the importance of machine learning is increasing according to the requests of fourth industrial revolution. This paper predicts monthly power transactions using MLP, RNN, LSTM, and ANFIS of neural network algorithms. Also, this paper used monthly electricity transactions for mount and money, final energy consumption, and diesel fuel prices for vehicle provided by the National Statistical Office, from 2001 to 2017. This paper learns each algorithm, and then shows predicted result by using time series. Moreover, this paper proposed most excellent algorithm among them by using RMSE.

Neural Network Application for Geothermal Heat Pump Electrical Load Prediction (지열 히트펌프 전기부하 예측을 위한 신경망 적용 방법)

  • Anindito, Satrio;Kang, Eun-Chul;Lee, Euy-Joon
    • Journal of the Korean Solar Energy Society
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    • v.32 no.3
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    • pp.42-49
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    • 2012
  • 신경망방법은 공학, 경영 그리고 정보기술과 같이 다양한 분양에서 널리 사용되어지고 있다. 신경망방법은 기본적으로 예측, 제어, 식별과 같은 기능을 가지고 있는데, 본 논문에서는 신경망방법을 이용하여 C사의 모델 T의 히트펌프 전기부하를 예측하였다. 부하예측은 시스템을 더욱 효율적이고, 적절하게 만들기 위해 필요하다. 본 논문에서 사용된 히트펌프는 지열원 히트 펌프 시스템이다. 이 지열 히트 펌프의 부하는 사전에 미리 예측되어진 외기온도 및 건물 열부하에 따라 측정 학습된 전력 소비량으로 겨울에는 난방, 여름에는 냉방에 대한 전력 부하를 예측할 수 있다. 이 신경망방법은 신경망 학습 순서를 통해 부하 예측을 위해 히트펌프의 성능데이터를 필요로 한다. 이 부하 예측 인공지능망 방법으로 외기 온도별 건물 통합형 지열 히트 펌프 부하가 예측되어질 수 있다.