• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.1
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• pp.262-268
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• 2017

The energy consumption of buildings is approximately 20.5% of the total energy consumption, and the interest in energy efficiency and low consumption of the building is increasing. Several studies have performed energy analysis and evaluation. Energy analysis and evaluation are effective when applied in the initial design phase. In the initial design phase, however, the energy performance is evaluated using general level information, such as glazing area and surface area. Therefore, the evaluation results of the detailed design stage, which is based on the drawings, including detailed information of the materials and facilities, will be different. Thus far, most studies have reported the analysis and evaluation at the detailed design stage, where detailed information about the materials installed in the building becomes clear. Therefore, it is possible to improve the accuracy of the energy environment analysis if the energy environment information generated during the life cycle of the building can be established and accurate information can be provided in the analysis at the initial design stage using a probability / statistical method. On the other hand, historical data on energy use has not been established in Korea. Therefore, this study performed energy environment analysis to construct the energy environment historical data. As a result of the research, information classification system, information model, and service model for acquiring and providing energy environment information that can be used for building lifecycle information of buildings are presented and used as the basic data. The results can be utilized in the historical data management system so that the reliability of analysis can be improved by supplementing the input information at the initial design stage. If the historical data is stacked, it can be used as learning data in methods, such as probability / statistics or artificial intelligence for energy environment analysis in the initial design stage.

• Proceedings of the Korean Society of Computer Information Conference
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• 2022.07a
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• pp.693-696
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• 2022

The purpose of this paper is to explore the factors affecting the purchase intention of new energy vehicles, utilizing the BRA and VAM models. Based on self-interest and altruism, a model for willingness to use new energy vehicles was developed. Through analysis with analytical tools such as SPSS and AMOS, we obtained the following conclusions: Perceived value, perceived entertainment, and environmental values all have a significant positive impact on the purchase intention of new energy vehicles; Perceived risk has a negative impact on purchase intention. By conducting this research, useful suggestions can be made for the formulation of enterprise strategies, and new directions and inspirations can be provided for enterprises.

• ETRI Journal
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• v.37 no.3
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• pp.450-459
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• 2015

Small cell networks, as an important evolution path for next-generation cellular networks, have drawn much attention. Different from the traditional base stations (BSs) always-on model, we proposed a BSs on-off model, where a new, simple expression for the probabilities of active BSs in a heterogeneous network is derived. This model is more suitable for application in practical networks. Based on this, we develop an analytical framework for the performance evaluation of small cell networks, adopting stochastic geometry theory. We derive the system coverage probability; average energy efficiency (AEE) and average uplink power consumption (AUPC) for different association strategies; maximum biased received power (MaBRP); and minimum association distance (MiAD). It is analytically shown that MaBRP is beneficial for coverage but will have some loss in energy saving. On the contrary, MiAD is not advocated from the point of coverage but is more energy efficient. The simulation results show that the use of range expansion in MaBRP helps to save energy but that this is not so in MiAD. Furthermore, we can achieve an optimal AEE by establishing an appropriate density of small cells.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.2
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• pp.21-25
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• 2023

The purpose of this study was to compare the performance using multiple regression models to predict the energy consumption of steel industry. Specific independent variables were selected in consideration of correlation among various attributes such as CO2 concentration, NSM, Week Status, Day of week, and Load Type, and preprocessing was performed to solve the multicollinearity problem. In data preprocessing, we evaluated linear and nonlinear relationships between each attribute through correlation analysis. In particular, we decided to select variables with high correlation and include appropriate variables in the final model to prevent multicollinearity problems. Among the many regression models learned, Boosted Decision Tree Regression showed the best predictive performance. Ensemble learning in this model was able to effectively learn complex patterns while preventing overfitting by combining multiple decision trees. Consequently, these predictive models are expected to provide important information for improving energy efficiency and management decision-making at steel industry. In the future, we plan to improve the performance of the model by collecting more data and extending variables, and the application of the model considering interactions with external factors will also be considered.

• KIEAE Journal
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• v.16 no.5
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• pp.13-20
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• 2016

Purpose: The purpose of this study is to provide information on building's energy consumption and efficiency for general building users through a mobile application. Method: This paper presents a mobile application process and building energy assessment models for general users to understand easily. There are two assessment models, one is based on the energy consumption. The other is based on the architectural planning factors of a building. The assessment models are proposed to understand buildings' energy efficiency and to compare the energy consumption level for general users. The applicability of proposed application has been evaluated by conducting a case study. The case study is targeting university buildings. Result: Energy efficiency potentials were proposed using weighting factor which was calculated by the impact on energy consumption of a building according to parameters. The mobile application used the simple energy assessment model by energy efficiency potentials and was developed for a smartphone By using the mobile application, numerous general users of smartphones can easily and conveniently access information pertaining to buildings, energy consumption, and reductions in energy consumption. The proposed application enables user to find more energy efficient buildings by comparing energy status and energy efficiency potential by given information.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.3
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• pp.819-837
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• 2014

Data centers, which implement cloud service, have been faced up with quick growth of energy consumption and low efficiency of energy. 60GHz wireless communication technology, as a new option to data centers, can provide feasible approach to alleviate the problems. Aiming at energy optimization in 60GHz wireless data centers (WDCs), we investigate virtualization technology to assign virtual resources to minimum number of servers, and turn off other servers or adjust them to the state of low power. By comprehensive analysis of wireless data centers, we model virtual network and physical network in WDCs firstly, and propose Virtual Resource Mapping Packing Algorithm (VRMPA) to solve energy management problems. According to VRMPA, we adopt packing algorithm and sort physical resource only once, which improves efficiency of virtual resource allocation. Simulation results show that, under the condition of guaranteeing network load, VPMPA algorithm can achieve better virtual request acceptance rate and higher utilization rate of energy consumption.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.31 no.12B
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• pp.1028-1036
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• 2006

Energy conservation is an important issue in wireless networks, especially for self-organized, low power, low data-rate impulse-radio ultra-wideband (IR-UWB) networks, where every node is a battery-driven device. To conserve energy, it is necessary to turn node into sleep state when no data exist. This paper addresses the energy consumption analysis of Direct-Sequence (DS) versus Time-Hopping (TH) multiple accesses and two kinds of sleeping protocols (slotted and unslotted) in PHY-MAC for Un networks. We introduce an analytical model for energy consumption or a node in both TH and DS multiple accesses and evaluate the energy consumption comparison between them and also the performance of the proposed sleeping protocols. Simulation results show that the energy consumption per packet of DS case is less than TH case and for slotted sleeping is less than that of unslotted one for bursty load case, but with respect to the load access delay unslotted one consumes less energy, that maximize node lifetime.

• Journal of the Korea Society of Computer and Information
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• v.24 no.3
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• pp.29-39
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• 2019

In this study, we introduce a model that satisfies energy efficiency and economical efficiency by introducing and demonstrating cogeneration generators in industrial complexes using various actual data collected at the site. The proposed model is composed of three scenarios, ie, full - time operation, scenario operated according to demand, and a fusion type. In this study, the power generation profit and surplus thermal energy are measured according to the operation of the generator, and the thermal energy is traded according to the demand of the customer to calculate the profit and loss including the heat and evaluate the economic efficiency. As a result of the study, it is relatively profitable to reduce the generation of the generator under the condition that the electricity rate is low and the gas rate is high, while the basic charge is not increased. On the contrary, if the electricity rate is high and the gas rate is low, The more you start up, the more profit you can see. These results show that even a cogeneration power plant with a low economic efficiency due to a low "spark spread" has sufficient economic value if it can sell more than a certain amount of heat energy from a nearby customer and adjust the applied power through peak management.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.5
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• pp.836-842
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• 2016

When an accident occurs in the nuclear power plant, the faulted information might mislead to the high possibility of aggravating the accident. At the Fukushima accident, the operators misunderstood that there was no core exposure despite in the processing of core damage, because the instrument information of the reactor water level was provided to the operators optimistically other than the actual situation. Thus, this misunderstanding actually caused to much confusions on the rapid countermeasure on the accident, and then resulted in multiplying the accident propagation. It is necessary to be equipped with the function that informs operators the status of instrument integrity in real time. If plant operators verify that the instruments are working properly during accident conditions, they are able to make a decision more safely. In this study, we have performed various tests for the fault detection sensitivity of an data-driven empirical model to review the usability of the model in the accident conditions. The test was performed by using simulation data from the compact nuclear simulator that is numerically simulated to PWR type nuclear power plant. As a result of the test, the proposed model has shown good performance for detecting the specified instrument faults during normal plant conditions. Although the instrument fault detection sensitivity during plant accident conditions is lower than that during normal condition, the data-drive empirical model can be detected an instrument fault during early stage of plant accidents.

• Nuclear Engineering and Technology
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• v.51 no.4
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• pp.1154-1162
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• 2019

China is undertaking an energy reform from fossil fuels to clean energy to accomplish $CO_2$ intensity (CI) reduction commitments. After hydropower, nuclear energy is potential based on breadthwise comparison with the world and analysis of government energy consumption (EC) plan. This paper establishes a CI energy policy response forecasting model based on national and provincial EC plans. This model is then applied in Fujian Province to predict its CI from 2016 to 2020. The result shows that CI declines at a range of 43%-53% compared to that in 2005 considering five conditions of economic growth in 2020. Furthermore, Fujian will achieve the national goals in advance because EC is controlled and nuclear energy ratio increased to 16.4% (the proportion of non-fossil in primary energy is 26.7%). Finally, the development of nuclear energy in China and the world are analyzed, and several policies for energy optimization and CI reduction are proposed.