• Journal of Advanced Information Technology and Convergence
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• v.10 no.1
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• pp.57-69
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• 2020

In general, many IoT devices, including smart phones, use LTE, Wi-Fi, and Bluetooth, and these communication modules generate a lot of energy consumption during periodic data transmission. This paper proposes a method of the data transmission mode change for improving energy efficiency in various communication environments that mobile devices may encounter. We propose an algorithm for setting the mode considering energy efficiency, data transmission performance and cost when the mobile device transmits data, and transmitting the data in an optimized manner according to the state of the mobile device. The proposed algorithm is implemented through experiments on energy efficiency for each communication module, and the scenario is used to verify how efficiently the proposed algorithm uses energy.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.1
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• pp.287-296
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• 2024

Artificial intelligence (AI), the Internet of Things (IoT), and Big Data are playing important roles in improving or upgrading energy efficiency. Furthermore, their roles in energy efficiency are expected to become more and more essential. This study conducted a bibliometric comparative analysis on the features in the articles on the AI, the IoT, and the Big Data in energy efficiency by using the Web of Science database and compared the features in their trends in article publications, citations, countries, research areas, journals, and funding agencies from 2012 to 2022. This study attempted to make significant contributions by shedding new light on the following features. Among the AI, the IoT, and the Big Data in energy efficiency, the most articles were published and the most article citations were received in the AI in energy efficiency. China was found out to be the most leading country. Engineering and computer science were revealed to be the first research area. IEEE Access and IEEE Internet of Things were ranked with first journal. National Natural Science Foundation of China was the first research funding agency concerning the articles published in the AI, the IoT, and the Big Data in energy efficiency from 2012 to 2022.

• Journal of Electrical Engineering and Technology
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• v.9 no.3
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• pp.1132-1136
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• 2014

In the context of increasing electric energy consumption in a data center, energy efficiency improvement is strongly emphasized. In a data center, electric energy is largely consumed by DC power supply system, which is based on a rectifier composed by multiple parallel converters. Therefore, rectifier efficiency must be improved for minimizing loss of DC power supply system. Rectifier efficiency can be modulated by load allocation to converters because converter efficiency depends on input AC power. In this paper, we propose a new control method to maximize rectifier efficiency. The method can control load allocation to converters by introducing active power converter control scheme and start-and-stop of converters. In order to illustrate optimal load allocations in a rectifier, a maximization problem of rectifier efficiency is formulated as a nonlinear optimization one. The problem is solved by Lagrangian relaxation method and the computation results provide the validity of proposed method.

• Journal of the Korean Society of Industry Convergence
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• v.15 no.3
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• pp.65-69
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• 2012

Since 2010, enhancement of the building energy efficiency and certification system and public office building should have been acquiring the first grade of Building Energy Efficiency. The Building Energy Efficiency Rating evaluation tool and Dynamic Analysis Energy simulation program for Building Energy Efficiency are widely used. The suitability to those programs have been discussed as a variety of programs have been used accordingly. In this study, evaluated the characteristic of Building Energy Efficiency Rating tool(ECO2) of the business building. At a result, the variables on the Weather Data, building Profile and building Load property in hourly between those Building Energy Efficiency evaluation tools have different.

• Journal of Korea Society of Digital Industry and Information Management
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• v.16 no.1
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• pp.33-39
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• 2020

A smart grid is a next-generation power grid that can improve energy efficiency by applying information and communication technology to the general power grid. The smart grid makes it possible to exchange information about electricity production and consumption between electricity providers and consumers in real-time. Advanced metering infrastructure (AMI) is the core technology of the smart grid. The AMI provides two-way communication by installing a modem in an existing digital meter and typically include smart meters, data collection units, and meter data management systems. Because the AMI requires data collection units to control multiple smart meters, it is essential to ensure network availability under heavy network loads. If the load on the work done by the data collection unit is high, it is necessary to allocation new data collection units to ensure availability and improve energy efficiency. In this paper, we discuss the allocation scheme of data collection units for the energy efficiency of the AMI.

• International Journal of Railway
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• v.6 no.2
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• pp.33-44
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• 2013

In recent years, energy consumption in the transportation sector by expanding motorization continues to increase in almost every country in the world. Moreover, the growth rate of the transportation energy consumption is significantly higher than those of the civilian and industrial sectors. Therefore, every country strives to reduce its dependence on private transport, which is the main contributor to the transportation energy consumption. In many countries, concepts such as Transit Oriented Development (TOD) or New Urbanism, which controls road traffic by increasing the proportion of the public transportation significantly, have been implemented to encourage a modal shift to public transport. However, the level of change required for eliminating environmental problems is a challenging task. Minimizing transportation energy consumption by controlling the increase of the traffic demand and maintaining the level of urban mobility simultaneously is a pressing dilemma for each city. Grasping the impact of the diversity of the urban transport and infrastructure is very important to improve transportation energy efficiency. However, the potential for reducing urban transportation energy consumption has often been ineffectively demonstrated by the diversity of cities. Therefore, the accuracy of evaluating the current efficiency rate of the urban energy consumption is necessary. Nevertheless, quantitative analyses related to the efficiency of transportation energy consumption are scarce, and the research on the current condition of consumption efficiency based on international quantitative analysis is almost nonexistent. On the basis of this background problem definitions, this research first built a database of the transportation energy consumption of private modes in 119 cities, with an attempt to reflect individual travel behaviors calculated by Person Trip data. Subsequently, Data Envelopment Analysis (DEA) was used as an assessment method to evaluate the efficiency of transportation energy consumption by considering the diversity of the urban traffic features in the world cities. Finally, we clarified the current condition of consumption efficiency by attempting to propose a target values for improving transportation energy consumption.

• Journal of Advanced Navigation Technology
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• v.20 no.5
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• pp.408-416
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• 2016

This study designed a ship energy efficiency monitoring system based on a ship application system that provides maritime services by utilizing data collected onboard, and a ship-land integration system for integrated management and exchange of maritime data. The ship energy efficiency monitoring system was developed as a Windows application program and designed to use file based EDI communications. Its main functions include route planning to minimize fuel consumption, monitoring of energy consumption and gas emissions, analysis of ship energy efficiency and other data analysis. The system has been successfully implemented in actual ships.

• Korean Management Science Review
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• v.32 no.3
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• pp.37-54
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• 2015

This paper suggests energy efficiency which can be the foundation on corporate profit and effective energy management following by change of global climate and of energy-related regulations. Using comparable financial information and information related to energy use, an DEA (Data Envelopment Analysis) model has been used to identify energy efficiency with DMU (Decision Making Unit)s which are companies subjected to reduce greenhouse gas emission in 2009. Through this research, different from existing researches, environmental variables which can influence on energy efficiency are identified. The results show as follows. First, most of companies follow IRS, which means scale of economy exists among units so that they have more opportunity to increase efficiency by increasing scale of inputs. Second, this research identified that depending on the difference of environmental characters such as the emission structure and the size of companies, energy efficiency of the companies turns out differently.

• Journal of ILASS-Korea
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• v.29 no.1
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• pp.1-6
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• 2024

This paper is a numerical analysis study for evaluating the energy efficiency of electric vehicles. Currently, the methods for testing and evaluating the energy consumption efficiency of electric vehicles have limitations such as resources and time. Therefore, there is a need for research on developing models to predict the energy consumption efficiency of electric vehicles. In this study, a numerical analysis research is conducted to predict the energy efficiency of electric vehicles using a vehicle dynamics numerical analysis model. To validate the accuracy of the simulation model, it is compared the results of dynamometer tests with the simulation results and used the Unified Diagnostic Services (UDS) protocol to acquire internal data from the electric vehicle. It is ensured the reliability of the simulation model by comparing data such as motor speed, battery voltage, current, state of charge (SOC), regenerative braking power generation, and total driving distance of the test vehicle with dynamometer test data and simulation model results.

• KIEAE Journal
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• v.17 no.2
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• pp.13-20
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• 2017

Meteorological data is one of the important factors in the calculation of building energy demand. The purposes of this study are to review the limitations of the typical meteorological data of ECO2 program and to create the new typical meteorological data and then analyze the building energy demands for additional regions which are not included in the existing 13 region in the ECO2 program. The extended typical meteorological data to a total of 33 regions were based on IWEC(International Weather for Energy Calculations) data files and were created in the form applicable to the building energy efficiency rating certification system. As a result of comparing the heating energy demands of a representative region with the surrounding regions in each of five regions in Korea, the variance of Cv(RMSE) ranged from 36% to 344% and MBE ranged from -32% to 190% for the whole regions. This suggests that the difference of heating energy demand may vary greatly depending on the region where the meteorological data is used and the meteorological data of more detailed regions is needed for reliable calculation of building energy demand.