• The Journal of Asian Finance, Economics and Business
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• v.7 no.1
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• pp.101-110
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• 2020

Energy and energy consumption play an important role in strategies for socio-economic development of the country. In 1995, Vietnam officially entered the 500 kV North-South transmission power line exploits, with a full length of 1,487 km. The purpose of this study is to investigate the breakpoint and the transition effect of energy consumption to economic growth in Vietnam during the period of 1980-1994, and 1995-2016. The Autoregressive Distributed Lag (ARDL) approach and the Bounds test are used to test for the presence of cointegration, whereas the Toda and Yamamoto procedure Granger causality test is used for the direction of causality. The result of the Bounds test validates the existence of cointegration among the included variables. The empirical results provide evidence that energy consumption has a positive impact on the economic growth of Vietnam in the long run. The causality test shows that there is bi-directional causality between energy consumption and economic growth, supported feedback hypothesis. There is a breakpoint in 1995 and the contribution of energy consumption in economic growth in the period of 1995-2016 is lower than the stage 1980-1994. This study suggests Government authorities explore new sources of energy to achieve sustainable economic development in the long run.

• The Journal of the Korea Contents Association
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• v.18 no.1
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• pp.422-430
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• 2018

Increase in oil prices and building energy consumption has been a great burden for Korea which has significant energy dependence on foreign energy sources. In this context, reduction of building energy consumption, which comprises 40% of total energy consumption, is a very important issue. This research therefore empirically analyzed a hospital "P" that implemented ICT-based energy consumption and cost reduction initiative. The hospital first replaced existing water absorber for heating/cooling air and boiler for heating water with water heat storage heat pump system. This was accompanied by subscribing to different electricity price plans to maximize cost reduction. Secondly, the hospital additionally applied ICT-based optimized control algorithm that considers surrounding factors (external temperature, changes in energy demand). The analysis of these mechanisms indicate that the ICT-based energy consumption and cost reduction initiative for hospitals can reduce energy consumption by 53.6% with replacement of low-efficiency equipment and additionally by 18.2% with optimized control algorithm. The mechanisms will provide energy consumption reduction opportunities for other hospitals and buildings with high energy consumption.

• Journal of Energy Engineering
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• v.22 no.1
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• pp.38-43
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• 2013

There was a dire need to compile data about energy consumption data by use to analyze residential energy consumption patterns relating to changes in lifestyles, or changes in life behavior. Accordingly, bottom-up model for residential energy consumption by residential use was developed by life behavior classification in an attempt to analyze energy consumption. This paper multiplied each appliance's running times by each appliance by life behavior and built a residential bottoms-up model to figure out the energy consumption of each household. The uses by life behavior were broken down into lighting, heating, cooling, entertainment, obtaining information, hygiene, and cooking.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.301-307
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• 2022

Energy consumption has grown alongside dramatic population increases. Statistics show that buildings in particular utilize a significant amount of energy, worldwide. Because of this, building energy prediction is crucial to best optimize utilities' energy plans and also create a predictive model for consumers. To improve energy prediction performance, this paper proposes a ResNet-LSTM model that combines residual networks (ResNets) and long short-term memory (LSTM) for energy consumption prediction. ResNets are utilized to extract complex and rich features, while LSTM has the ability to learn temporal correlation; the dense layer is used as a regression to forecast energy consumption. To make our model more robust, we employed Huber loss during the optimization process. Huber loss obtains high efficiency by handling minor errors quadratically. It also takes the absolute error for large errors to increase robustness. This makes our model less sensitive to outlier data. Our proposed system was trained on historical data to forecast energy consumption for different time series. To evaluate our proposed model, we compared our model's performance with several popular machine learning and deep learning methods such as linear regression, neural networks, decision tree, and convolutional neural networks, etc. The results show that our proposed model predicted energy consumption most accurately.

• Journal of the Korean Solar Energy Society
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• v.36 no.6
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• pp.61-69
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• 2016

In this study, we analyzed five-year(2011~2015) data for D high school in Seoul area to analyze energy consumption characteristics in high school. The results are summarized as follows. (1) In the result of comparison analysis about 2015 energy consumption by usage, based on primary energy, 18% of energy was consumed in cafeteria, and 82% was consumed in main building. In the case of main building, base and constant load excepting hot water supply in restroom took 40%, heating including freeze protection took 20%, hot water supply in restroom took 14%, and cooling took 8% in order. (2) In the 2015 total energy consumption in D high school based on primary energy, heating energy takes 28%. The range and limit of energy savings coming from the reinforcement of insulation and window performance could be estimated. (3) To introduce new & renewable energy system in high school, electricity-based system is suitable than heat-based system because usage of electric energy is larger than that of heat energy in high school. (4) Five-year energy consumption unit according to heating degree-day showed a linearly increasing trend, and the coefficient of determination(R2) was 0.9763, which means high correlation.

• 월간 기계설비
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• s.127
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• pp.40-50
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• 2001

The study focused on the analysis of energy consumption of hotel in Jeju. For this study, we regionally investigated the energy consumption in Jeju and Seogwipo by questionnaires. Based on the fundamental data, we classified the energy consumption accordi

• International Journal of Air-Conditioning and Refrigeration
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• v.6
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• pp.1-13
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• 1998

The basic heat transfer process that occurs in a building can best be illustrated by an electrical circuit network. Present paper reports the dynamic simulation of annual energy consumption in an office building by the thermal resistance capacitance network method. Unsteady thermal behaviors and annual energy consumption in an office building were examined in detail by solving the simultaneous circuit equations of thermal network. The results are used to evaluate the accuracy of the modified BIN method for the energy consumption analysis of a large building. Present thermal resistance-capacitance method predicts annual energy consumption of an office building with the same accuracy as that of response factor method. However, the modified BIN method gives 15% lower annual heating load and 25% lower cooling load than those from the present method. Equipment annual energy consumptions for fan, boiler and chiller in the HVAC system are also calculated for various control systems as CAV, VAV, FCU+VAV and FCU+CAV. FCU+CAV system appears to consume minimum annual energy among them.

• 한국태양에너지학회:학술대회논문집
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• 2011.11a
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• pp.130-133
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• 2011

This paper reports the Survey results from a field monitoring study of office occupancy densities. The field measurement of a office in Yongin was carried out from 19 September to 30 September 2011. The survey has an aim to reveal the building energy consumption relationship between occupancy densities of a realistic office and the previous studies. The results showed that hourly occupied density of the previous studies is more higher than a field survey. we investigated the effects of difference occupancy densities on annual heating and cooling energy consumption using EnergyPlus. Heating and cooling consumption was raised because of the increased occupancy density. therefore, accurately measure the occupnacy schedule is important in order to reduce excessive building energy consumption, and is an significant element to be considered in the energy simulation.

• Child Health Nursing Research
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• v.25 no.1
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• pp.48-55
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• 2019

Purpose: This study aimed to investigate the status of caffeine-containing energy drink consumption among Korean nursing students and to identify associated psychological factors. Methods: In total, 187 Korean nursing students participated in this cross-sectional study. A self-administered questionnaire was used to identify participants' general characteristics and psychosocial factors (self-esteem, academic stress, depression, and college adjustment) associated with energy drink consumption. Data were analyzed with SPSS using descriptive statistics, the $x^2$ test, the t-test, and logistic regression. Results: More than two-thirds (73.3%) of the participants had consumed energy drinks. Among the investigated psychological factors, depression appeared to most strongly influence energy drink consumption behaviors in this population. Conclusion: The consumption of caffeine-containing energy drinks was found to be common among nursing students preparing to become health care professionals; depressed nursing students were more likely to have consumed energy drinks than non-depressed students. Nursing educators should emphasize the early detection of unhealthy beverage consumption habits and provide appropriate education to enhance healthy behaviors in future health care professionals.

• International conference on construction engineering and project management
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• 2015.10a
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• pp.537-539
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• 2015

considerate operation is a major issue in the equipment-intensive operation. Identifying an optimal equipment combination is important to achieve low-energy operations. An Earthwork operation planning system, which measures the energy consumption of construction operations by taking into account construction equipments' engineering attributes (e.g., weight, capacity, energy consumption rate, etc.) and operation conditions (e.g., road condition, attributes of materials to be moved, geometric information, etc.), is essential to achieve the low-energy consumption. This study develops an automated computerized system which identifies an optimal earthmoving equipment fleet minimizing the energy consumption. The system imports a standard template of earthmoving operation model and compares numerous scenarios using alternative equipment allocation plans. It finds the fleet that minimizes the energy consumption by enumerating all cases using sensitivity analysis. A case study is presented to verify the validity of the system.