• 한국태양에너지학회:학술대회논문집
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• 2008.04a
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• pp.304-310
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• 2008

Currently energy use planning council system is mandatory especially for the urban development project planned on a specified scale or more. The goal of existing demand prediction was to calculate the maximum load by multiplying energy load per unit area by building size. The result of this method may be exaggerated and has a limit in the information of period load. The paper suggests a new forecasting process based on standard unit household in order to upgrade the limit in demand prediction method of multi-family housing complex. The new process was verified by comparing actual using amount of multi-family housing complex to forecasting value of energy use plan.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.14 no.11
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• pp.2569-2574
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• 2010

In this paper, we introduce an efficient energy management using a notion of virtual energy system for shared solar-powered sensor network. Virtual energy system is an abstraction that allows sensor network applications on a node to reserve their own fractions of the shared solar cell and the shared rechargeable battery, hence achieving logically partition of a shared renewable power source. Our results show that our design and implementation are reliable, lightweight and efficient, allowing proper isolation of energy consumption among applications.

• Environmental and Resource Economics Review
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• v.28 no.4
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• pp.467-495
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• 2019

Uncertainty of renewable energy such as photovoltaic(PV) power is detrimental to the flexibility of the power system. Therefore, precise prediction of PV power generation is important to make the power system stable. The purpose of this study is to forecast PV power generation using meteorological data including particulate matter(PM). In this study, PV power generation is predicted by support vector machine using RBF kernel function based on machine learning. Comparing the forecasting performances by including or excluding PM variable in predictor variables, we find that the forecasting model considering PM is better. Forecasting models considering PM variable show error reduction of 1.43%, 3.60%, and 3.88% in forecasting power generation between 6am~8pm, between 12pm~2pm, and at 1pm, respectively. Especially, the accuracy of the forecasting model including PM variable is increased in daytime when PV power generation is high.

• Proceedings of the Korean Vacuum Society Conference
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• 2013.02a
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• pp.673-673
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• 2013

최근 시대의 흐름에 따라 많은 에너지의 사용으로 여러 가지 에너지원이 필요로 하게 되면서 지금까지는 석탄, 석유 등 매장된 에너지원을 사용하고 있지만, 최근 에너지 위기와 여러 가지의 환경문제가 대두 되면서 세계적으로 새로운 청정에너지원을 필요로 하게 되었다. 그 결과 태양광, 풍력, 지열 등 여러 가지의 신재생에너지원이 대두되게 되었으며, 여러 가지의 신재생에너지원 중 주목받고 있는 풍력에너지에 대한 연구가 현재 활발히 진행 중에 있다. 풍력발전은 바람의 에너지를 이용해 블레이드에 연결된 터빈을 구동하여 전기 에너지를 얻는 방식이며, 아직까지는 많은 곳에서 사용될 만큼 생산이 되지 않고 있지만 조만간 많은 곳에서 쓰일 것으로 예상된다. 풍력발전 시스템이 전력시장에서 차지하는 비중이 점차 증가하고 있으나 풍향, 풍속 등의 변화로 인하여 안정적인 발전 출력을 항상 보장할 수 없다. 그러므로 본 논문에서는 실제 풍력발전기로부터 수집된 풍향, 풍속, 발전출력 데이터를 처리하여 데이터베이스를 구축하고, 퍼지 뉴런에 기반한 퍼지-뉴럴 네트워크 예측 모델을 이용하여 풍력발전 출력을 예측하였다.

• Solar Energy
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• v.11 no.2
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• pp.9-19
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• 1991

Lighting is one of the largest energy consumption in commercial building. For saving such lighting energy, integrated lighting system with daylight and artificial lighting has been suggested. In such system, perimeter zone can be illuminated by daylighting and the deep area of room by artificial lighting. So, the study aimed to develope of lighting energy prediction nomograph by turnning-off depth and lighting control systems during daytime. For the purpose, energy nomo-graph has been developed to apply to side-lit office building and the use and limitation of the nomograph has been discussed.

• Journal of the Korean Solar Energy Society
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• v.37 no.1
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• pp.25-38
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• 2017

Climate change and environmental pollution are becoming serious due to the use of fossil energy. For this reason, renewable energy systems are increasing, especially photovoltaic systems being more popular. The photovoltaic system has characteristics that are affected by ambient weather conditions such as insolation, outside temperature, wind speed. Particularly, it has been confirmed that the performance of the photovoltaic system decreases as the module temperature increases. In order to grasp the influence of the module temperature in advance, several researchers have proposed the prediction models on the module temperature. In this paper, we predicted the module temperature using the aforementioned prediction model on the basis of the weather conditions in Incheon, South Korea during July and August. The influence of weather conditions (i.e. insolation, outside temperature, and wind speed) on the accuracy of the prediction models was also evaluated using the standard statistical metrics such as RMSE, MAD, and MAPE. The results show that the prediction accuracy is reduced by 3.9 times and 1.9 times as the insolation and outside temperature increased respectively. On the other hand, the accuracy increased by 6.3 times as the wind speed increased.

• Journal of IKEEE
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• v.23 no.1
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• pp.334-337
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• 2019

In response to the increasing demand for energy, research and development of next-generation energy is actively carried out around the world to replace fossil fuels. Among them, the specific gravity of solar power generation systems using infinity and pollution-free solar energy is increasing. However, solar power generation is so different from solar energy that it is difficult to provide stable power and the power production itself depends on the solar energy by region. To solve these problems in this paper, we have collected meteorological data such as actual regional solar irradiance, precipitation, temperature and humidity, and proposed a solar power output prediction system using logic-based fuzzy Neural Network.

• Smart Media Journal
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• v.12 no.10
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• pp.63-70
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• 2023

Recently, energy consumption for heating costs, which is 35% of smart farm energy costs, has increased, requiring energy consumption efficiency, and the importance of new and renewable energy is increasing due to concerns about the realization of electricity bills. Renewable energy belongs to hydropower, wind, and solar power, of which solar energy is a power generation technology that converts it into electrical energy, and this technology has less impact on the environment and is simple to maintain. In this study, based on the greenhouse heat storage tank and heat pump data, the factors that affect the heat storage tank are selected and a heat storage tank supply temperature prediction model is developed. It is predicted using Long Short-Term Memory (LSTM), which is effective for time series data analysis and prediction, and XGBoost model, which is superior to other ensemble learning techniques. By predicting the temperature of the heat pump heat storage tank, energy consumption may be optimized and system operation may be optimized. In addition, we intend to link it to the smart farm energy integrated operation system, such as reducing heating and cooling costs and improving the energy independence of farmers due to the use of solar power. By managing the supply of waste heat energy through the platform and deriving the maximum heating load and energy values required for crop growth by season and time, an optimal energy management plan is derived based on this.

• 한국태양에너지학회:학술대회논문집
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• 2012.03a
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• pp.506-510
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• 2012

This study suggested an improved algorithm of urban energy consumption estimation on the urban planning stage which concerns calculation accuracy. The results are as follows. (1) Urban energy consumption was estimated and managed per unit space using E-GIS DB which contains facility information per mesh. (2) Urban energy consumption was reflected by the urban facility classified and standardized by the characteristics of energy use. (3) Calculation accuracy of energy consumption was approached by separately suggested as summer algorithm reflecting urban heat island on summer energy use and winter algorithm reflecting heating system normally used in Korea.

• Solar Energy
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• v.20 no.1
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• pp.45-54
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• 2000

Five direct/diffuse decomposition models were validated using the eight years data set of direct normal beam insolation measured in Seoul. The comparison has been performed In terms of the widely used statistical indicators such as MBE, RMSE, CV(RMSE), t-Statistic and Degree of Agreement. Result indicates that most of the correlations exhibit a tendency to underestimate the direct normal beam insolation except Bouguer's model. Most of big discrepancies between the measured and the predicted values was mainly shown in near the sunrising and the sunset period. Even though the investigated five models showed fairly large disagreement for the measured values by 34%$\sim$48% of CV(RMSE), Udagawa's correlation which includes the effect of solar altitude variation appears to performs always better in every statistical error tests.