• Journal of KIISE:Computing Practices and Letters
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• v.15 no.7
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• pp.478-488
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• 2009

Using solar power in wireless sensor networks (WSNs) requires adaptation to a highly varying energy supply and to a battery constraint. From an application's perspective, however, it is often preferred to operate at a constant quality level as opposed to changing application behavior frequently. Reconciling the varying supply with the fixed demand requires good tools for allocating energy such that average of energy supply is computed and demand is fixed accordingly. In this paper, we propose a probabilistic observation-based model for harvested solar energy. Based on this model, we develop a time-slot-based energy allocation scheme to use the periodically harvested solar energy optimally, while minimizing the variance in energy allocation. We also implement the testbed and demonstrate the efficiency of the approach by using it.

• Journal of Environmental Science International
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• v.19 no.10
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• pp.1229-1235
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• 2010

Three meteor-statistical forecasting models - the transfer function model, the time-series autoregressive model and the neural networks model - were tested to develop a daily forecasting model for Jejudo, where the need and demand for wind power forecasting has increased. All the meteorological observation sites in Jejudo have been classified into 6 groups using a cluster analysis. Four pairs of observation sites among them, all having strong wind speed correlation within the same meteorological group, were chosen for a model test. In the development of the wind speed forecasting model for Jejudo, it was confirmed that not only the use a wind dataset at the objective site itself, but the introduction of another wind dataset at the nearest site having a strong wind speed correlation within the same group, would enhance the goodness to fit of the forecasting. A transfer function model and a neural network model were also confirmed to offer reliable predictions, with the similar goodness to fit level.

• The Korean Journal of Applied Statistics
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• v.32 no.5
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• pp.703-720
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• 2019

The influence of temperature on electricity demand is increasing due to extreme weather and climate change, and the climate impacts involves nonlinearity, asymmetry and complexity. Considering changes in government energy policy and the development of the fourth industrial revolution, it is important to assess the climate effect more accurately for stable management of electricity supply and demand. This study aims to analyze the effect of temperature change on electricity demand using the partial linear model. The main results obtained using the time-unit high frequency data for meteorological variables and electricity consumption are as follows. Estimation results show that the relationship between temperature change and electricity demand involves complexity, nonlinearity and asymmetry, which reflects the nonlinear effect of extreme weather. The prediction accuracy of in-sample and out-of-sample electricity forecasting using the partial linear model evidences better predictive accuracy than the conventional model based on the heating and cooling degree days. Diebold-Mariano test confirms significance of the predictive accuracy of the partial linear model.

• Journal of the Korean Data and Information Science Society
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• v.27 no.2
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• pp.275-284
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• 2016

Accurate electricity demand forecasts is essential in reducing energy spend and preventing imbalance of the power supply. In forcasting electricity demand, we considered double seasonal Holt-Winters model and TBATS model with sliding window. We selected a specific time zone as the reference line of daily electric demand because it is least likely to be influenced by external factors. The forecasting performance have been evaluated in terms of RMSE and MAPE criteria. We used the observations ranging January 4, 2009 to December 31 for testing data. For validation data, the records has been used between January 1, 2012 and December 29, 2012.

• Atmosphere
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• v.24 no.4
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• pp.515-522
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• 2014

In this study it is attempted to estimate the possible change in energy use for residential and commercial sector in Korea under a future climate change senario. Based on the national energy use and observed temperature data during the period 1991~2010, the optimal base temperature for determining heating and cooling degree days (HDD and CDD) is calculated. Then, net changes in fossil fuel and electricity uses that are statistically linked with a temperature variation are quantified through regression analyses of HDD and CDD against the energy use. Finally, the future projection of energy use is estimated by applying the regression model and future temperature projections by the CMIP5 results under the RCP8.5 scenario. The results indicate that, overall, the net annual energy use will decrease mostly due to a large decrease in the fossil fuel use for heating. However, a clear seasonal contrast in energy use is anticipated in the electricity use; there will be an increase in a warm-season demand for cooling but a decrease in a cold-season demand for heating.

• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.87-91
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• 1990

Regional electricity demand forecasting is among the most important step for lone-term investment and power supply planning. This study presents a regional electricity forecasting model for Korean power system. The model consists of three submodels, regional economy, regional electricity energy demand, and regional peak load submodels. A case study is presented.

• Journal of Energy Engineering
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• v.24 no.1
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• pp.104-113
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• 2015

This study tried to conduct a comparative analysis on the yearly economic effects of petroleum products sector. Inter-industry tables published 1990~2012 are used in this study. Especially petroleum products sector is specified as exogenous to identify the economic effects on own and other sectors. Production-inducing effect, value-added creation effect, and employment-inducing effect are quantified based on demand-driven model. The results of the analysis, the case of annual production inducing-effect, show the downward trend from 1993 to 2008. and It seemed to be constant from 2009 to 2012. The value-added inducing-effect, from 1990 to 1998, shows a rising trend. the since 1998, it was found to decline steadily. Employment-inducing effect is shown a steadily decreasing trend from 1990 to 2008, and has been kept constant from 2010 at the level under 1.300(person/one billion won). These results of in comparison with the past are significant in that it can be objectively evaluate the domestic oil industry at the present time. and it can be usefully utilized to predict the economic effect of future oil industry.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.4
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• pp.733-738
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• 2011

As increase of nonlinear loads and renewable energy generators (REGs) being connected to power distribution system via inverters, the concern on harmonic problems have increased. Recently, the harmonics evaluation method considering TDD (Total Demand Distortion) is used to analyze the effect of harmonics from inverters on power distribution quality. Harmonic current sources are typically used for simulation of nonlinear load. Most inverter type for REGs is voltage source inverter (VSI). So, harmonic voltage sources are more suitable to analyze impact of renewable energy generator on harmonics problem in power distribution system. In this paper, we presented the circuit model to analyze interaction between harmonics from nonlinear load and REGs. We verified that the harmonic analysis using the proposed circuit model is more appropriate than the harmonics evaluation method considering TDD through case study using PSCAD/EMTDC.

• Nuclear Engineering and Technology
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• v.36 no.2
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• pp.175-183
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• 2004

For the analysis of a dry process fuel cycle, new modules were implemented into the fuel cycle analysis code DYMOND, which was developed by the Argonne National Laboratory. The modifications were made to the energy demand prediction model, a Canada deuterium uranium (CANDU) reactor, direct use of spent pressurized water reactor (PWR) fuel in CANDU reactors (DUPIC) fuel cycle model, the fuel cycle calculation module, and the input/output modules. The performance of the modified DYMOND code was assessed for the postulated once-through fuel cycle models including both the PWR and CANDU reactor. This paper presents modifications of the DYMOND code and the results of sample calculations for the PWR once-though and DUPIC fuel cycles.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2001.04a
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• pp.371-378
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• 2001

To enhance seismic performance of a structure ATC-40 and FEMA-273 propose technical strategies such as increasing strength, altering stiffness, and reducing demand by employing base isolation and energy dissipation devices. Specifically the energy dissipation devices directly increase the ability of the structure to dampen earthquake response. However nonlinear dynamic time history analysis of a structure with energy dissipation devices is complicated and time consuming. In this study a simple and straightforward procedure is developed using effective damping ratio to obtain the required amount of viscoelastic dampers in order to meet given performance objectives. Parametric study has been performed for the period of the structure, yield strength, and the stiffness after the first yield. According to the analysis results, earthquake demand and required damping ratio were reduced by installing viscoelastic dampers. The results also show that with the addition of the supplemental damping evaluted by the proposed method the performance of the model structures are well restrained within the target point.