• Journal of Energy Engineering
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• v.28 no.4
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• pp.82-93
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• 2019

In this study, a new model is proposed to improve the problem of the decline of predict rate of heat demand on a particular date, such as a public holiday for the conventional heat demand forecasting system. The proposed model was the Four Season Mixed Heat Demand Prediction Neural Network Model, which showed an increase in the forecast rate of heat demand, especially for each type of forecast date (weekday/weekend/holiday). The proposed model was selected through the following process. A model with an even error for each type of forecast date in a particular season is selected to form the entire forecast model. To avoid shortening learning time and excessive learning, after each of the four different models that were structurally simplified were learning and a model that showed optimal prediction error was selected through various combinations. The output of the model is the hourly 24-hour heat demand at the forecast date and the total is the daily total heat demand. These forecasts enable efficient heat supply planning and allow the selection and utilization of output values according to their purpose. For daily heat demand forecasts for the proposed model, the overall MAPE improved from 5.3~6.1% for individual models to 5.2% and the forecast for holiday heat demand greatly improved from 4.9~7.9% to 2.9%. The data in this study utilized 34 months of heat demand data from a specific apartment complex provided by the Korea District Heating Corp. (January 2015 to October 2017).

• Environmental and Resource Economics Review
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• v.9 no.5
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• pp.925-945
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• 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 Korean Operations Research and Management Science Society
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• v.8 no.2
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• pp.45-56
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• 1983

This paper presents a generalized fuel choice model in which restrictive constraints on cross-price coefficients as Baughman-Joskow-FEA Logit Model need not be imposed, but all demand elasticities are uniquely determined. The model is applied to estimating aggregate energy demand and fuel choices for the residential and commercial sector. The structural equations are estimated by a generalized least squares procedure using national-level EPB, KDI, BK, KRIS, MOER data for 1965 and 1980, and other related reports. The econometric results support the argument that “third-price” and “fourth-price” coefficients should not be constrained in estimating relative market share models. Furthermore, by using this fuel choice model, it has forecasted energy demands by fuel sources in, the residential and commercial sector until 1991. The results are turned out good estimates to compare with existing demands forecasted from other institutes.

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

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.58 no.4
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• pp.505-510
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• 2009

In this paper, building energy analysis and energy cost of power stand up and demand control over the power proposed to reduce power demand. Through analysis of the load power demand special day were able to apply the pattern. In addition, the existing rate of change of load forecasting to reduce the large errors were not previously available data. And daily schedules and special day for considering the exponential smoothing methods were used. Previous year's special day and the previous day due to the uncertainty of the load and the model components were considered. The maximum demand power control simulation using the fuzzy control of power does not exceed the contract. Through simulation, the benefits of the proposed energy-saving techniques were demonstrated.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.64 no.2
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• pp.74-78
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• 2015

With the improvement of living standards and economic development, electricity consumption continues to grow. The electricity is a special energy which is hard to store, so its supply must be consistent with the demand. The objective of electricity demand forecasting is to make best use of electricity energy and provide balance between supply and demand. Hence, it is very important work to forecast electricity demand with higher precision. So, various forecasting methods have been developed. They can be divided into five broad categories such as time series models, regression based model, artificial intelligence techniques and fuzzy logic method without considering special-day effects. Electricity demand patterns on holidays can be often idiosyncratic and cause significant forecasting errors. Such effects are known as special-day effects and are recognized as an important issue in determining electricity demand data. In this research, we developed the power demand forecasting method using ELM(Extreme Learning Machine) for special day, particularly, lunar new year and Chuseok holiday.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.220-220
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• 2015

Water, energy, and food security already became a risk that threatens people around the world. Increasing of resources demand, rapid urbanization, decreasing of natural resources and climate change are four major problems inducing resources' scarcity. Indeed, water, energy, and food are interconnected each other thus cannot be analyzed separately. That is, for simple example, energy needs water as source for hydropower plant, water needs energy for distribution, and food needs water and energy for production, which is defined as W-E-F nexus. Due to their complicated linkage, it needs a computer model to simulate and analyze the nexus. Development of a computer simulation model using system dynamics approach makes this linkage possible to be visualized and quantified. System dynamics can be defined as an approach to learn the feedback connections of all elements in a complex system, which mean, every element's interaction is simulated simultaneously. Present W-E-F nexus models do not calculate and simulate the element's interaction simultaneously. Existing models only calculate the amount of water and energy resources that needed to provide food, water, or energy without any interaction from the product to resources. The new proposed model tries to cope these lacks by adding the interactions, climate change effect, and government policy to optimize the best options to maintain the resources sustainability. On this first phase of development, the model is developed only to learn and analyze the interaction between elements based on scenario of fulfilling the increasing of resources demand, due to population growth. The model is developed using the Vensim, well-known system dynamics model software. The results are amount of total water, energy, and food demand and production for a certain time period and it is evaluated to determine the sustainability of resources.

• Journal of the Korean Society for Geothermal and Hydrothermal Energy
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• v.17 no.1
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• pp.1-13
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• 2021

Before newly-built building and building remodeling, it is important to predict and analyze building energy performance through energy simulation programs. Nevertheless, simulation results widely vary depending on individual user experience and input values. Therefore, this study uses prototypical building model, a versatile tool in building energy modeling, simulation and research for researchers and policy-makers, and ASHRAE standards. Then, it analyzed the changes in design type (roof type, number of floors) for the base case. As the result, it was found that the gap of annual energy demand per between them is maximally 9.1%.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.1
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• pp.97-115
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• 2022

Smart power grid is a user friendly system that transforms the traditional electric grid to the one that operates in a co-operative and reliable manner. Demand Response (DR) is one of the important components of the smart grid. The DR programs enable the end user participation by which they can communicate with the electricity service provider and shape their daily energy consumption patterns and reduce their consumption costs. The increasing demands of electricity owing to growing population stresses the need for optimal usage of electricity and also to look out alternative and cheap renewable sources of electricity. The solar and wind energy are the promising sources of alternative energy at present because of renewable nature and low cost implementation. The proposed work models a smart home with renewable energy units. The random nature of the renewable sources like wind and solar energy brings an uncertainty to the model developed. A stochastic dual descent optimization method is used to bring optimality to the developed model. The proposed work is validated using the simulation results. From the results it is concluded that proposed work brings a balanced usage of the grid power and the renewable energy units. The work also optimizes the daily consumption pattern thereby reducing the consumption cost for the end users of electricity.

• KIEAE Journal
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• v.13 no.2
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• pp.77-84
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• 2013

Schools are one of favorable public buildings for Renewable Energy(RE) systems due to site conditions and their energy demand profiles(e.g. daytime-based use of hot water and heating/cooling). Although the government encourages schools to be equipped with RE systems, the adoption of RE systems in existing energy supply systems faces technical and financial barriers. For example, when installing a RE-based combination system(RECS) to meet the energy demand at various school scales, identifying cost effective combination of capacities of the RECS is not trivial since it usually requires technically intensive work including detailed simulation and demand/supply analysis with extensive data. This kind of simulation-based approaches is hardly implementable in practice. To address this, a simpler and applicable decision-supporting method is suggested in this study. This paper presents a simplified model in support of decision-making for optimal capacities of RECS within given budget scales and schools sizes. The proposed model was derived from detailed simulation results and statistical data. Using this model, the optimal capacities of RECS can be induced from the number of classes in a school.