• 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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.8
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• pp.1111-1115
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• 2014

With the increasing interest of a demand side management using a Smart Grid infrastructure, the demand resources and energy usage data management becomes an important factor in energy industry. In addition, with the help of Advanced Measuring Infrastructure(AMI), energy usage data becomes a Big Data System. Therefore, it becomes difficult to store and manage the demand resources big data using a traditional relational database management system. Furthermore, not many researches have been done to analyze the big energy data collected using AMI. In this paper, we are proposing a Hadoop based Big Data system to manage the demand resources energy data and we will also show how the demand side management systems can be used to improve energy efficiency.

• Journal of the Korean Solar Energy Society
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• v.39 no.3
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• pp.67-79
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• 2019

The reliable Typical Meteorological Year (TMY) data, sometimes called Test Reference Year (TRY) data, are necessary in the feasibility study of renewable energy installation as well as zero energy building. In Korea, there are available TMY data; TMY from Korea Institute of Energy Research (KIER), TRY from the Korean Solar Energy Society (KSES) and TRY from Passive House Institute Korea (PHIKO). This study aims at examining their characteristics by using Principle Component Analysis (PCA) at six ground observing stations. First step is to investigate the annual averages of meteorological elements from TMY data and their standard deviations. Then, PCA is done to find which principle components are derived from different TMY data. Temperature and solar irradiance are determined as the main principle component of TMY data produced by KIER and KSES at all stations whereas TRY data from PHIKO does not show similar result from those by KIER and KSES.

• Journal of the Korean Solar Energy Society
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• v.27 no.1
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• pp.19-27
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• 2007

The Data quality management system(DQMS) organizes and helps manage and process time sequence data usually collected in monitoring networks and programs. DQMS places particular emphasis on data qualify while maintaining a highly organized and convenient structure for data. It operates with in a flexible and powerful commercial relational data base environment which can readily link to other software platforms from local spreadsheets to network server. The Korea Institute of Energy Research(KIER) has been solar radiation data since May, 1991 for 16 different locations. KIER's new data is expected to be extensively used by designer and researchers of solar systems in lieu of unreliable old ones. Unfortunately, the quality of the data has not always been properly mentioned. The purpose of this study is to systematically identify errors in such data set using DQMS in an effort to rehabilitate error-ridden old data. DET successfully uncovered solar radiation data that had questionable quality.

• Advances in Energy Research
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• v.5 no.4
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• pp.321-339
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• 2017

Energy simulation tools can provide information on the amount of heat transfer through building envelope components, which are considered the main sources of heat loss in buildings. Therefore, it is important to improve the quality of outputs from energy simulation tools and also the process of obtaining them. In this paper, a new Building Energy Performance Assessment Tool (BEPAT) is introduced, which provides users with granular data related to heat transfer through every single wall, window, door, roof, and floor in a building and automatically saves all the related data in text files. This information can be used to identify the envelope components for thermal improvement through energy retrofit or during the design phase. The generated data can also be adopted in the design of energy smart homes, building design tools, and energy retrofit tools as a supplementary dataset. BEPAT is developed by modifying EnergyPlus source code as the energy simulation engine using C++, which only requires Input Data File (IDF) and weather file to perform the energy simulation and automatically provide detailed output. To validate the BEPAT results, a computer model is developed in Revit for use in BEPAT. Validating BEPAT's output with EnergyPlus "advanced output" shows a difference of less than 2% and thus establishing the capability of this tool to facilitate the provision of detailed output on the quantity of heat transfer through walls, fenestrations, roofs, and floors.

• Journal of the Korean Solar Energy Society
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• v.32 no.2
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• pp.35-41
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• 2012

Knowledge of the solar radiation components and classified wavelength data are essential for modeling many solar photovoltaic systems. This is particularly the case for applications that concentrate the incident energy to attain high photo-dynamic efficiency achievable only at the higher intensities. In order to estimate the performance of concentrating PV systems, it is necessary to know the intensity of the beam radiation, as only this components can be concentrated, and The new PV cell can generate electricity from ultraviolet and infrared light as well as visible light. The Korea Institute of Energy Research(KIER) has began collecting solar radiation components data since January, 1988, and solar radiation classified wavelength data since November, 2008. KIER's solar radiation components and classified wavelength data will be extensively used by concentrating PV system users or designers as well as by research institutes. It is essential to utilize the solar radiation data as application and development of solar energy system increase. Consider able efforts have been made constructing a standard data base system from measure data.

• KIEAE Journal
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• v.17 no.5
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• pp.77-86
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• 2017

Purpose: Because of the growing concern over fossil fuel use and increasing demand for greenhouse gas emission reduction since the 1990s, the building energy analysis field has produced various types of methods, which are being applied more often and broadly than ever. A lot of research products have been actively proposed in the area of the building energy simulation for over 50 years around the world. However, in the last 20 years, there have been only a few research cases where the trend of building energy analysis is examined, estimated or compared. This research aims to investigate a trend of the building energy analysis by focusing on methodology and characteristics of each method. Method: The research papers addressing the building energy analysis are classified into two types of method: engineering analysis and algorithm estimation. Especially, EPG(Energy Performance Gap), which is the limit both for the existing engineering method and the single algorithm-based estimation method, results from comparing data of two different levels- in other words, real time data and simulation data. Result: When one or more ensemble algorithms are used, more accurate estimations of energy consumption and performance are produced, and thereby improving the problem of energy performance gap.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.893-895
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• 2016

Data center has a lot of management efforts for the facility, energy, and efficient usage monitoring. Data center power management is important to make the data center have reliable service and cost-effective business. In this paper, IoT based energy measurements monitoring which gives support to energy consumption analysis including indoor, outdoor temperature condition. This converged information for energy analysis gives various aspects of energy consumption effects. With IoT big data, energy machine learning system can give the relation of energy components and measurements, it is the key information of the quick energy analysis in the just one month data trend for the prediction and estimation.

• 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.

• 한국태양에너지학회:학술대회논문집
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• 2011.04a
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• pp.63-67
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• 2011

KnowledgThe Korea Institute of Energy Research(KIER) has begun collecting horizontal global insolation data since May, 1982 at different locations. Because of a poor reliability of existing data, KIER's new data will be extensively used by the solar system users as well as by research institutes. But the quality of solar insolation data is not always good. This reports on an attempt to identify systematic error in such data using clear-day analysis for data rehabilitation. Clear-day analysis is successful in uncovering solar insolation data of questionable quality. It is not proven that rehabilitation process can improve the quality of data for daily or monthly means, but it is suggested that the method can be used to improve the quality of data for monthly means of several years for use in many applications of solar energy plarming. Earlier studies finding a maximum ETR of about 0.80 are confirmed.