• International journal of advanced smart convergence
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• v.10 no.4
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• pp.59-65
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• 2021

Electricity contributes to the development of the economy. Therefore, forecasting electricity demand plays an important role in the development of the electricity industry in particular and the economy in general. This study aims to provide a precise model for long-term electricity demand forecast in the residential sector by using three independent variables include: Population, Electricity price, Average annual income per capita; and the dependent variable is yearly electricity consumption. Based on the support of Multiple variable regression, the proposed method established a model with variables that relate to the forecast by ignoring variables that do not affect lead to forecasting errors. The proposed forecasting model was validated using historical data from Vietnam in the period 2013 and 2020. To illustrate the application of the proposed methodology, we presents a five-year demand forecast for the residential sector in Vietnam. When demand forecasts are performed using the predicted variables, the R square value measures model fit is up to 99.6% and overall accuracy (MAPE) of around 0.92% is obtained over the period 2018-2020. The proposed model indicates the population's impact on total national electricity demand.

• Journal of the Korea Society of Computer and Information
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• v.26 no.5
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• pp.141-148
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• 2021

The purpose of this study is to explore the interaction of virtual reality consumption pattern under the background of virtual reality, and the influence of such interaction on consumers' purchase intention. In this paper, we selected three independent variables (including content interaction, function interaction and service interaction) and two mediating variables (including perceived quality and perceived value) to explore the relationship between them and consumers' purchase intention, used SPSS24.0 and AMOS24.0, made the analysis of reliability and validity, constructed the structural equation model, and tested the hypotheses. The findings show that: fourteen hypotheses are all true, and the perceived value of virtual reality has a positive influence on consumers' purchase intention. In addition, we find that the perceived value has a mediating effect in this study.

• KIEAE Journal
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• v.16 no.2
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• pp.23-31
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• 2016

Purpose: Recently energy consumption is rapidly increasing due to continuous development of social evolution in various field. In this situation, there is a lot of effort to reduce this energy consumption in many ways, especially in building energy. Preceding studies already started to analyze the housing area such as zero energy house and passive house by researching annual building energy consumption, but to apply the results of housing to office building is insufficient since it has different consumption tendency. Method: In this study, eQuest program was used for simulation and the base model is selected among standard office building in ASHRAE 90.1. Variables are divided into passive and active factors for comparison. Result: In passive factors, glazing system showed the highest energy saving rate by 21.3% with triple low-e glass and enhancing wall u-value showed the lowest energy saving rate by 3.6% with 0.15 m2/K. In active factors, VAV system showed 30.9% energy saving rate when compared to CAV system, and heat exchanger showed 10.2% energy saving rate. For regeneration energy part, photovoltaic panel generated 10.4% of base annual energy usage.

• International Journal of Highway Engineering
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• v.19 no.3
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• pp.19-30
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• 2017

PURPOSES : This study proposes the road asset valuation approach using alternative depreciation methods. It has become necessary to have asset management system according to the adoption of accrual basis accounting for governmental financial reporting and the amendment of the road act. Therefore, it is very important to analyze the effect of depreciation methods on road asset value as a basic research for road asset management system. METHODS : The Ministry of Strategy and Finance (MOSF) has mainly performed road asset valuation based on Write down Replacement Cost and Straight Line depreciation method. This study suggests some appropriate asset valuation methods for road assets through case analysis using three depreciation methods: Consumption-based depreciation method, Condition-based depreciation method, and Straight Line depreciation method. A road asset valuation data of national highway route 1 (year 2014) is used to analyze the effect of three depreciation methods on the road asset value. Road assets include land and structures (pavement, bridge, and tunnel). This study mainly focuses on structures such as bridges and tunnels, because according to governmental accounting standards, land and road pavement assets do not depreciate. RESULTS : The main results of this study are as follows. Firstly, overall asset value of national highway route 1 was estimated at 6.97 trillion KRW when MOSF's method (straight-line depreciation method) is applied. Secondly, asset value was estimated at 4.85 trillion KRW on application of consumption-based depreciation method. Thirdly, asset value was estimated at 4.37 trillion KRW when condition-based depreciation method is applied. Therefore, either consumption-based or condition-based depreciation methods would be more appropriate than straight-line depreciation method if we can use the condition data of road assets including land that are available in real time. CONCLUSIONS : Since road assets such as pavements, bridges, and tunnels have various patterns of deterioration and condition monitoring period, it is necessary to consider a specific valuation method according to the condition of each road asset. Firstly, even though road pavements do not depreciate, asset valuation through condition-based depreciation method would be more appropriate when requirements for application of non-depreciation approach are not satisfied. Since bridge and tunnel facilities show various patterns of deterioration and condition monitoring period by type and condition level, consumption-based depreciation method based on deterioration model would be appropriate. Therefore, it is necessary to have a reasonable asset management system to apply condition-based depreciation method and a periodic condition investigation to manage road assets well.

• 한국노년학
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• v.29 no.1
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• pp.195-213
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• 2009

The purpose of this study is to analyse how consumption value affects the loyalty of green product through satisfaction and trust among elderly consumers. Data were collected from a cross-sectional survey of 314 older adults (age≥60) in the U. S., who bought and possessed a hybrid car, a representative green product. The statistical methodology is employed a structural equation model. The results demonstrated several important findings. First, perceived social value among elderly population had significant effects on green product satisfaction, while hedonic value did not. Second, both perceived functional value and environment friendly value had a significant positive effect on trust in green products. Third, satisfaction with green products also led to trust in green products. Finally, trust in green products showed their significant effects on loyalty in green product. These results provide practical implications to improve the trust and loyalty in green products among the elderly consumers. Furthermore, by deriving major components of consumption values in green products among the elderly, and analyzing the mechanism of satisfaction, trust, and loyalty, the study emphasizes relationship marketing in implementing "green" marketing strategies.

• Journal of the Architectural Institute of Korea Structure & Construction
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• v.34 no.11
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• pp.73-79
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• 2018

In this study, for the prediction of energy consumption in the ventilator, one of the components of the air conditioning system, the predicted results were analyzed and accurate by the change in the number of neurons and inputs. The input variables of the prediction model for the energy volume of the fan were the supply air flow rate, the exhaust air flow rate, and the output value was the energy consumption of the fan. A predictive model has been developed to study with the Levenbarg-Marquardt algorithm through 8760 sets of one-minute resolution. Comparison of actual energy use and forecast results showed a margin of error of less than 1% in all cases and utilization time of less than 3% with very high predictability. MBE was distributed with a learning period of 1.7% to 2.95% and a service period of 2.26% to 4.48% respectively, and the distribution rate of ${\pm}10%$ indicated by ASHRAE Guidelines 14 was high.8.

• KIEAE Journal
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• v.12 no.3
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• pp.41-46
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• 2012

The building envelope is important specially for saving energy consumption of residential buildings. but Apartment houses in Korea commonly have inside insulation system which have constantly arisen thermal bridges, the risk of heat loss, as a necessity. This study aims to evaluate integrated insulation performance according to the different shapes of external walls, adjacent to windows. The thermal performance analysis was carried out by Equivalent U-value and using the three-dimensional heat transfer computer simulation (TRISCO-RADCON), under nine different cases of comparing among three each of different bases(current standard model, 30percent energy saving model and 60percent energy saving model). The heating and the cooling load were also compared between two cases (standard U-value and Equivalent U-value) of three each of different bases, using the Building energy simulation which is based on DOE-2.1 analysis. As results, it turns out that if the Equivalent U-value is considered on the envelope analysis, the heat flow loss will be increasing more than the standard U-value, and if heat insulation property of the residential building reinforced rather than current, the rate of influences on the thermal bridges would be extremely expanded. In addition, it is shown that annual heating loads of the apartment house with applied Equivalent U-value substantially increased by more than 15 percent compared to those with the existing U-value, but annual cooling loads were negligibly affected.

• Food Science of Animal Resources
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• v.35 no.5
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• pp.674-682
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• 2015

The objective of this study was to evaluate the risk of illness from Campylobacter spp. on ham. To identify the hazards of Campylobacter spp. on ham, the general characteristics and microbial criteria for Campylobacter spp., and campylobacteriosis outbreaks were investigated. In the exposure assessment, the prevalence of Campylobacter spp. on ham was evaluated, and the probabilistic distributions for the temperature of ham surfaces in retail markets and home refrigerators were prepared. In addition, the raw data from the Korea National Health and Nutrition Examination Survey (KNHNES) 2012 were used to estimate the consumption amount and frequency of ham. In the hazard characterization, the Beta-Poisson model for Campylobacter spp. infection was used. For risk characterization, a simulation model was developed using the collected data, and the risk of Campylobacter spp. on ham was estimated with @RISK. The Campylobacter spp. cell counts on ham samples were below the detection limit (<0.70 Log CFU/g). The daily consumption of ham was 23.93 g per person, and the consumption frequency was 11.57%. The simulated mean value of the initial contamination level of Campylobacter spp. on ham was −3.95 Log CFU/g, and the mean value of ham for probable risk per person per day was 2.20×10⁻¹². It is considered that the risk of foodborne illness for Campylobacter spp. was low. Furthermore, these results indicate that the microbial risk assessment of Campylobacter spp. in this study should be useful in providing scientific evidence to set up the criteria of Campylobacter spp..

• Fire Science and Engineering
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• v.30 no.2
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• pp.92-97
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• 2016

The present study has been conducted to estimate the chemical flame height based on fuel consumption in fire field model. The calculation algorithms based on cumulative fraction of HRRPUL and fuel concentration along the z axis were applied to the results predicted by Fire Dynamics Simulator (FDS) version 6.3.2 and the mean chemical flame height was obtained by time averaging of instantaneous flame height with the algorithms. The mean flame height calculated by fuel concentration was quite well matched with that of cumulative value of HRRPUL within 10% over-prediction. This study contribute to a more detailed understanding of fire behavior and quantitative evaluation of flame height in the computational fire model.

• KIPS Transactions on Software and Data Engineering
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• v.9 no.5
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• pp.153-160
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• 2020

Energy Consumption Predictions for Industries has a prominent role to play in the energy management and control system as dynamic and seasonal changes are occurring in energy demand and supply. This paper introduces and explores the steel industry's predictive models of energy consumption. The data used includes lagging and leading reactive power lagging and leading current variable, emission of carbon dioxide (tCO2) and load type. Four statistical models are trained and tested in the test set: (a) Linear Regression (LR), (b) Radial Kernel Support Vector Machine (SVM RBF), (c) Gradient Boosting Machine (GBM), and (d) Random Forest (RF). Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) are used for calculating regression model predictive performance. When using all the predictors, the best model RF can provide RMSE value 7.33 in the test set.