• Journal of the Korean Solar Energy Society
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• v.33 no.1
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• pp.48-56
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• 2013

Energy consumption analysis of multi-family apartment is an important area of research for the design of energy-saving housing. In this study, we selected a universal type of Flat-type apartments and analyzed the heating energy consumption of variables such as U-value, G-value, infiltration rate, heating setpoint and boiler efficiency with EnergyPlus and eQUEST. With these results, we identify the characteristics of EnergyPlus and eQUEST and provided base data for the design of energy-saving housing. The results indicate that infiltration rate is the most important factors to consider. And eQUEST heating energy consumption is approximately 10% higher compared to the EnergyPlus under same condition.

• Environmental Engineering Research
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• v.21 no.4
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• pp.409-419
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• 2016

As a major energy consumption province, the issue about the carbon emissions in Hebei Province, China has been concerned by the government. The carbon emissions can be effectively reduced due to a more rational energy consumption structure. Thus, in this paper the constraint of low carbon emissions is considered as a foundation and four energies--coal, petroleum, natural gas and electricity including wind power, nuclear power and hydro-power etc are selected as the main analysis objects of the adjustment of energy structure. This paper takes energy cost minimum and carbon trading cost minimum as the objective functions based on the economic growth, energy saving and emission reduction targets and constructs an optimization model of energy consumption structure. And empirical research about energy consumption structure optimization in 2015 and 2020 is carried out based on the energy consumption data in Hebei Province, China during the period 1995-2013, which indicates that the energy consumption in Hebei dominated by coal cannot be replaced in the next seven years, from 2014 to 2020, when the coal consumption proportion is still up to 85.93%. Finally, the corresponding policy suggestions are put forward, according to the results of the energy structure optimization in Hebei Province.

• Journal of Communications and Networks
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• v.12 no.2
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• pp.103-113
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• 2010

In this paper, a tradeoff between the total energy consumption-per-bit and the end-to-end rate under spatial reuse in wireless multi-hop network is developed and analyzed. The end-to-end rate of the network is the number of information bits transmitted (end-to-end) per channel use by any node in the network that is forwarding the data. In order to increase the bandwidth efficiency, spatial reuse is considered whereby simultaneous relay transmissions are allowed provided there is a minimum separation between such transmitters. The total energy consumption-per-bit includes the energy transmitted and the energy consumed by the receiver to process (demodulate and decoder) the received signal. The total energy consumption-per-bit is normalized by the distance between a source-destination pair in order to be consistent with a direct (single-hop) communication network. Lower bounds on this energy-bandwidth tradeoff are analyzed using convex optimization methods. For a given location of relays, it is shown that the total energy consumption-per-bit is minimized by optimally selecting the end-to-end rate. It is also demonstrated that spatial reuse can improve the bandwidth efficiency for a given total energy consumption-per-bit. However, at the rate that minimizes the total energy consumption-per-bit, spatial reuse does not provide lower energy consumption-per-bit compared to the case without spatial reuse. This is because spatial reuse requires more receiver energy consumption at a given end-to-end rate. Such degraded energy efficiency can be compensated by varying the minimum separation of hops between simultaneous transmitters. In the case of equi-spaced relays, analytical results for the energy-bandwidth tradeoff are provided and it is shown that the minimum energy consumption-per-bit decreases linearly with the end-to-end distance.

• KIEAE Journal
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• v.12 no.2
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• pp.77-82
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• 2012

Data mining is to discover problems in the large amounts of data. Also, data mining trying to find the cause of the problem and the structure. Building energy consumption patterns, the amount of data is infinite. Also, the patterns have a lot of direct and indirect effects. Discussion is needed about the correlation. This work looking for the cause of energy consumption. As a result, energy management can find out the issue. Building energy analysis utilizing data mining techniques to predict energy consumption. And the results are as follows: 1) Using data mining technique, We classified complicated data to several patterns and gained meaningful informations from them. 2) Using cluster analysis, We classified building energy consumption data of residents and analyzed characters of patterns.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.24 no.8
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• pp.612-620
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• 2012

High economic growth causes increase of the building energy consumption. The energy consumption for HVAC system accounts for 40~50% of the whole building consumption. The trend for building is large-scale and high-rise. Because of the trend, the energy consumption is becoming bigger than before. Nowadays, HVAC system design are recognized as the solution for a energy-saving. This paper is focused on the energy performance evaluation of central air-conditioning system(water-based) and system air-conditioning that were applied to the office building. The systems are modeled and simulated by using EnergyPlus Software 6.0. After the Simulation, annual cooling and heating energy consumption were calculated. It was found that the system air-conditioning can reduce the energy consumption approximately 55.24% annually compared with the central air-conditioning system(water-cooled). In addition, about 46.13% of annual operating costs can be reduced by use of system air-conditioning.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.29 no.11
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• pp.605-611
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• 2017

The purpose of this study is to calculate the energy consumption rate based on data regarding energy use in office buildings, and to confirm the general characteristics of energy consumption. The energy consumption rate of the building is calculated by dividing the energy consumption by the floor area. The energy consumption rate of small-sized office buildings was calculated as $101.48{\sim}201.55kWh/m^2{\cdot}year$ and in the case of medium-sized buildings, the range was $92.77{\sim}177.89kWh/m^2{\cdot}year$. In the case of small buildings, it was found that the energy consumption was $73.24kWh/m^2{\cdot}year$ in electronic device, $34.31kWh/m^2{\cdot}year$ in hot water supply, and $18.37kWh/m^2{\cdot}year$ in heating. In the case of medium-sized buildings, electronic devices was $73.08kWh/m^2{\cdot}year$, lighting was $18.35kWh/m^2{\cdot}year$ and heating, $15.37kWh/m^2{\cdot}year$. In all of the study buildings, the peak heating energy use was observed from 8:00 a.m. to 10:00 a.m during the winter, and the peak power management was required. Energy use at and around the midnight hour is confirmed to be 40~60% of weekly working hours, so it is necessary to manage power use at night time as well as during the day. In order to improve the accuracy of future studies, it is necessary to make efforts to secure the data with standardized energy measuring units for the various type of buildings.

• Asia Pacific Journal of Business Review
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• v.7 no.1
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• pp.23-47
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• 2022

Environmental pollution is becoming more and more serious, and people gradually realize the harmfulness of environmental pollution, so they pay more and more attention to environmental problems. Also, the conflict between environmental issues and economic growth, and the renewable energy consumption is increasing. The emergence of renewable energy in China has improved the problem of energy shortages and further protects the environment. This article studied the renewable energy resources and the status quo of development and utilization, examined China's renewable energy development countermeasures and suggestions, and conducted an empirical analysis of the effect of renewable energy on economic growth in China. The empirical research concluded that energy consumption and renewable energy consumption have a positive and significant impact on economic growth, and the driving effect of traditional energy on GDP growth is still greater than the driving effect of renewable energy on GDP growth.

• 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.

• International journal of advanced smart convergence
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• v.7 no.3
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• pp.101-109
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• 2018

In this paper, we propose an adaptive filtering scheme of connection requests for the defense of malicious energy consumption attacks against wireless computing devices with limited energy budget. The energy consumption attack tries to consume the battery energy of a wireless device with repeated connection requests and shut down the wireless device by exhausting its energy budget. The proposed scheme blocks a connection request of the energy consumption attack in the middle, if the same connection request is repeated and its request result is failed continuously. In order to avoid the blocking of innocuous mistakes of normal users, the scheme gives another chance to allow connection request after a fixed blocking time. The scheme changes the blocking time adaptively by comparing the message arriving ate during non-blocking period and that during blocking period. Evaluation shows that the proposed defense scheme saves up to 94% energy consumption compared to the non-defense case.

• 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.