• 한국신재생에너지학회:학술대회논문집
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• 2007.06a
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• pp.721-724
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• 2007

Estimating of LFG emission rate has been well established. But, relatively short history of LFG extraction in Korea, the data and the formular have not been well compared and analyzed. Here, even though the operation period of extraction for Daegu Bangcheon-ri is short, the relevant correlation between estimated and the actual has been tried to find. Hopefully, this will be a guideline for the future LFG forecasting.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.711-712
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• 2006

In this paper, we propose algorithm that improves energy efficiency of sensor node. That is, sensor node suggests algorithm that minimizes unnecessary surrounding feeler, and when passes information to neighborhood node, transmit forecasting position of node.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.54 no.11
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• pp.557-560
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• 2005

This paper proposes the demand-side management(DSM) monitoring system of high efficient end-use equipments considering demand-side bidding (DSB). The effect on the market penetration of high efficient equipments by demand side bidding is analysed. Seasonal peak demand forecasting and penetration capacity of these equipments . are analysed.

• KEPCO Journal on Electric Power and Energy
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• v.6 no.4
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• pp.433-438
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• 2020

Accurate forecasting of wind power is important for grid operation. Wind power has intermittent and nonlinear characteristics, which increases the uncertainty in wind power generation. In order to accurately predict wind power generation with high uncertainty, it is necessary to analyze the factors affecting wind power generation. In this paper, 6 factors out of 11 are selected for more accurate wind power generation forecast. These are wind speed, sine value of wind direction, cosine value of wind direction, local pressure, ground temperature, and history data of wind power generated.

• Journal of the Korean housing association
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• v.23 no.6
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• pp.31-38
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• 2012

In this paper, we draws tendency of the electricity consumption in residential buildings according to inhabitants Composition types and the level of incomes. it is necessary to reduce energy cost and keep energy security through the electricity demand forecasting and management technology. Progressive social change such as increases of single household, the aging of society, increases in the income level will replace the existing residential electricity demand pattern. However, Only with conventional methods that using only the energy consumption per-unit area are based on Energy final consumption data can not respond to those social and environmental change. To develop electricity demand estimation model that can cope flexibly to changes in the social and environmental, In this paper researches propensity of electricity consumption according to the type of residents configuration, the level of income. First, we typed form of inhabitants in residential that existed in Korea. after that we calculated hourly electricity consumption for each type through National Time-Use Survey performed at the National Statistical Office with considering overlapping behavior. Household appliances and retention standards according to income level is also considered.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.5
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• pp.680-687
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• 2018

Because of its predictability of the energy cycle and huge scale power output, the ocean energy from tidal power utilization has always received attention as a great energy source, even though its development cost, including the embankment construction, is so much higher than that of any other energy source. Nevertheless, nowadays many projects are being planned on account of institutional support from the government and the recent advance of construction technology. In Korea, the new industry field operating and managing the tidal power plant has already opened. But we are facing a number of problems for optimal operation of tidal power plant that are a lack of operation experience and a skill of professional management and others. This paper suggests a novel way to determine the starting head of power generation by generating power comparison method For this new method, the paper discusses many factors including changing the volume of the basin, the number of operating turbines and gates and forecasting the tidal amplitude and the characteristic curve of turbine and gate. Finally we verified that it can increase about 2% an annual power generation compared with the conventional method using the original operational function made in the plant design process.

• Journal of Environmental Science International
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• v.23 no.6
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• pp.1085-1093
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• 2014

We investigate the amount of potential electricity energy generated by wind power in Busan metropolitan area, using the mesoscale meteorological model WRF (Weather Research & Forecasting), combined with small wind power generators. The WRF modeling has successfully simulated meteorological characteristics over the urban areas, and showed statistical significant to predict the amount of wind energy generation. The highest amount of wind power energy has been predicted at the coastal area, followed by at riverbank and upland, depending on predicted spatial distributions of wind speed. The electricity energy prediction method in this study is expected to be used for plans of wind farm constructions or the power supplies.

• 한국태양에너지학회:학술대회논문집
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• 2009.11a
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• pp.68-73
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• 2009

Since the atmospheric clearness index is main factor for evaluating atmosphere environment, it is necessary to estimate its characteristics all over the major cities in Korea Peninsula. We have begun collecting clearness index data since 1982 at 16 different cities in South Korea and estimated using empirical forecasting models at 21 different stations over the North Korea from 1982 to 2008. This considerable effort has been made for constructing a standard value from measured data at each city. The new clearness data for global-dimming analysis will be extensively used by evaluating atmospheric environment as well as by solar PV application system designer or users. From the results, we can conclude that. 1) Yearly mean 63.5 % of the atmospheric clearness index was evaluated for clear day all over the 37 cities in Korea Peninsula, 2) Clear day's atmospheric clearness index of spring and summer were 64.6% and 64.8%, and for fall and winter their values were 63.3% and 61.3% respectively in Korea Peninsula.

• Journal of Electrical Engineering and Technology
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• v.13 no.5
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• pp.1874-1885
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• 2018

The customer side operation is getting more complex in a smart grid environment because of the adoption of renewable resources. In performing energy management planning or scheduling, it is essential to forecast non-controllable resources accurately and robustly. The PV system is one of the common renewable energy resources in customer side. Its output depends on weather and physical characteristics of the PV system. Thus, weather information is essential to predict the amount of PV system output. However, weather forecast usually does not include enough solar irradiation information. In this study, a PV system power output prediction model (PPM) under limited weather information is proposed. In the proposed model, meteorological radiation model (MRM) is used to improve cloud cover radiation model (CRM) to consider the seasonal effect of the target region. The results of the proposed model are compared to the result of the conventional CRM prediction method on the PV generation obtained from a field test site. With the PPM, root mean square error (RMSE), and mean absolute error (MAE) are improved by 23.43% and 33.76%, respectively, compared to CRM for all days; while in clear days, they are improved by 53.36% and 62.90%, respectively.

• Journal of Korean Institute of Architectural Sustainable Environment and Building Systems
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• v.12 no.6
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• pp.627-640
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• 2018

Submetering electricity consumption data enables more detail input of end use components, such as lighting, plug, HVAC, and occupancy in building energy modeling. However, such an modeling efforts and results are rarely tried and published in terms of the estimation accuracy of electricity demand. In this research, actual submetering data obtained from a university building is analyzed and provided for building energy modeling practice. As alternative modeling cases, conventional modeling method (Case-1), using reference schedule per building usage, and main metering data based modeling method (Case-2) are established. Detail efforts are added to derive prototypical schedules from the metered data by introducing variability index. The simulation results revealed that Case-1 showed the largest error as we can expect. And Case-2 showed comparative error relative to Case-3 in terms of total electricity estimation. But Case-2 showed about two times larger error in CV (RMSE) in lighting energy demand due to lack of End Use consumption information.