• Environmental Engineering Research
• /
• v.16 no.1
• /
• pp.19-27
• /
• 2011

This paper introduces an approach to identify the total energy consumption with subsequent $CO_2$ emissions, for both industrial and non-industrial sectors. Statistical data for 2005 were compiled in a national account system to construct an energy input-output table for investigating the influence between energy demand and supply activities. The methodological approach was applied to South Korea. Twelve types of energy and fifteen industrial and non-industrial sectors are formed as the compartments of the input-output table. The results provided quantitative details of the energy consumption and identified the significant contributions from each sector. An impact analysis on the $CO_2$ emissions for the demand side was also conducted for comparison with the supply side.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.65 no.6
• /
• pp.946-954
• /
• 2016

Energy industry is undergoing a paradigm shift in customer participation in the smartgrid. Customers traditionally consume electrical power. But nowadays not only do they generate electricity from private distributed generations, they can participate in demand response programs with their negawatt power which means a theoretical unit of power representing an amount of energy saved. Therefore development of decision-making criteria for electric load aggregation becomes a greater consideration as an amount of energy saved from demand response resources increases. This paper proposes load aggregators' decision-making criteria in the industrial sector where it made up the largest portion in demand response portfolio in order to assure reliability performance for demand response resources.

• Environmental and Resource Economics Review
• /
• v.9 no.5
• /
• pp.925-945
• /
• 2000

This paper analyses the impact of energy-saving investment on Greenhouse gas emissions using a model of energy demand in Korea. SUR method was employed to estimate the demand equation. The econometric estimates provide information about the energy price divisia index, sector income, and energy saving-investment elasticities of energy demand. Except for energy price divisia, the elasticities of each variable are statistically significant. Also, the price and substitution elasticities of each energy price are similar to the results reported by the previous studies. The energy-saving investment is statistically significant and elasticities of each sector is inelastic. Using the coefficient of energy-saving investment and carbon transmission coefficient, the amount of reduction of energy demand and the reduction of carbon emissions can be estimated. The simulation is performed with the scenario that the energy-saving investment increase by 10~50%, keeping up with Equipment Investment Plan of 30% increase in energy-saving investment by 2000. The results show that the reduction of energy demand measured as 11.2% based upon 1995's level of the energy demand, in industrial sector. Accordingly, the carbon emissions will be reduced by 11.3% based upon 1995's level of the carbon emissions in industrial sector.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.13 no.8
• /
• pp.3307-3314
• /
• 2012

The annual energy demand of the standard rural house models was analyzed using the DesignBuilder. Indoor temperature set-point, U-value of outer wall, type of window, and degree of ventilation were selected as simulation parameters. In all the simulation cases, heating energy demand was higher than cooling energy demand regardless of the building size. When the lower U-value of the outer wall was applied to account for the thicker insulation layer, heating energy demand was decreased while cooling energy demand was increased. However, it is better to reduce the area of outer wall which is directly exposed to outdoor air because reducing the U-value of the outer wall is not effective in decreasing heating energy demand. Among the four different window types, the double skin window is most favorable because heating energy demand is the lowest. For a fixed infiltration rate, higher ventilation rate resulted in an increased heating energy demand and had minor impact on cooling energy demand. As long as the indoor air quality is acceptable, lower ventilation rate is favorable to reduce the annual energy demand.

• Transactions of the Korean hydrogen and new energy society
• /
• v.17 no.1
• /
• pp.90-97
• /
• 2006

Hydrogen is used as a chemical feedstock in several important industrial processes, including oil refineries and petro-chemical production. But, nowadays hydrogen is focused as energy carrier on the rising of problems such as exhaustion of fossil fuel and environmental pollution. Thermochemical hydrogen production by nuclear energy has potential to efficiently produce large quantities of hydrogen without producing greenhouse gases, and research of nuclear hydrogen, therefore, has been worked with goal to demonstrate commercial production in 2020. The oil refineries and petro-chemical plant are very large, centralized producers and users of industrial hydrogen, and high-potential early market for hydrogen produced by nuclear energy. Therefore, it is essential to investigate and analyze for state of domestic hydrogen market focused on industrial users. Hydrogen market of petro-chemical industry as demand site was investigated and worked for demand forecast of hydrogen in 2020. Also we suggested possible supply plans of nuclear hydrogen considered regional characteristics and then it can be provided basis for determination of optimal capacity of nuclear hydrogen plant in 2020.

• Transactions of the Korean hydrogen and new energy society
• /
• v.33 no.5
• /
• pp.591-597
• /
• 2022

Recently, technologies for efficient power grid operation have become important due to climate change. For this reason, predicting power demand using deep learning is being considered, and it is necessary to understand the influence of characteristics of each region, industrial structure, and climate. This study analyzed the power demand of New Jersey in US, with a high urbanization rate and a large service industry, and West Virginia in US, a low urbanization rate and a large coal, energy, and chemical industries. Using recurrent neural network algorithm, the power demand from January 2020 to August 2022 was learned, and the daily and weekly power demand was predicted. In addition, the power grid operation based on the power demand forecast was discussed. Unlike previous studies that have focused on the deep learning algorithm itself, this study analyzes the regional power demand characteristics and deep learning algorithm application, and power grid operation strategy.

• The Transactions of the Korean Institute of Electrical Engineers P
• /
• v.66 no.1
• /
• pp.7-14
• /
• 2017

Recently, the power consumption of industrial consumer has increased rapidly, causing problems such as lack of power reserve margin in summer and winter, and therefore there is a growing need for maximum demand power management to consumers. In this paper, we studied small microgrid system consisting of battery ESS and photovoltaic power system, applied to small and medium sized factories to reduce the maximum demand power of daily industrial power load. To verify the validity of the study, we simulated a small microgrid system using Matlab/Simulink software. As a result of applying the simulation to small and medium sized plants that consume a lot of power, it is confirmed that there is a 13% reduction in demand compared to the existing maximum demand power. This result is expected to contribute to the improvement of the power reserve margin.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.42 no.1
• /
• pp.129-136
• /
• 2019

In this study, we proposed a model for forecasting power energy demand by investigating how outside temperature at a given time affected power consumption and. To this end, we analyzed the time series of power consumption in terms of the power spectrum and found the periodicities of one day and one week. With these periodicities, we investigated two time series of temperature and power consumption, and found, for a given hour, an approximate linear relation between temperature and power consumption. We adopted an exponential smoothing model to examine the effect of the linearity in forecasting the power demand. In particular, we adjusted the exponential smoothing model by using the variation of power consumption due to temperature change. In this way, the proposed model became a mixture of a time series model and a regression model. We demonstrated that the adjusted model outperformed the exponential smoothing model alone in terms of the mean relative percentage error and the root mean square error in the range of 3%~8% and 4kWh~27kWh, respectively. The results of this study can be used to the energy management system in terms of the effective control of the cross usage of the electric energy together with the outside temperature.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.65 no.7
• /
• pp.1128-1134
• /
• 2016

Demand response provides customer load reductions based on high market prices or system reliability conditions. One type of demand response, price-based program, induces customers to respond to changes in product rates. However, there are large-scale general and industrial customers that have difficulty changing their energy consumption patterns, even with rate changes, due to their electricity demands being commercial and industrial. This study proposes an in-house pricing model for large-scale general and industrial customers, particularly those with multiple business facilities, for self-regulating demand-side management and cost reduction. The in-house pricing model charges higher rates to customers with lower load factors by employing peak to off-peak ratios in order to reduce maximum demand at each facility. The proposed scheme has been applied to real world and its benefits are demonstrated through an example.

• IE interfaces
• /
• v.24 no.4
• /
• pp.351-361
• /
• 2011

Forecasting of electricity demand have difficulty in adapting to abrupt weather changes along with a radical shift in major regional and global climates. This has lead to increasing attention to research on the immediate and accurate forecasting model. Technically, this implies that a model requires only a few input variables all of which are easily obtainable, and its predictive performance is comparable with other competing models. To meet the ends, this paper presents an energy demand forecasting model that uses the variable selection or extraction methods of data mining to select only relevant input variables, and employs support vector regression method for accurate prediction. Also, it proposes a novel performance measure for time-series prediction, shift index, followed by description on preprocessing procedure. A comparative evaluation of the proposed method with other representative data mining models such as an auto-regression model, an artificial neural network model, an ordinary support vector regression model was carried out for obtaining the forecast of monthly electricity demand from 2000 to 2008 based on data provided by Korea Energy Economics Institute. Among the models tested, the proposed method was shown promising results than others.