• Journal of Internet of Things and Convergence
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• v.7 no.3
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• pp.9-14
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• 2021

This paper discovers the energy loss factors through the insulation diagnosis of houses or buildings, and proposes directions for energy efficiency improvement. The energy efficiency factor of a building consists of insulation diagnosis, thermal bridge diagnosis, window diagnosis, airtight diagnosis, and equipment diagnosis. Among the residents and facilities in the energy welfare blind spot, an energy efficiency diagnosis was conducted for one senior citizen building located in Naju-si, Jeollanam-do, and energy efficiency diagnosis was conducted after insulation was installed. Energy measurement, diagnosis and analysis were performed using the IoT-based integrated wired/wireless energy diagnosis platform, Energy Finder. As a result of comparison, an overall energy saving rate of 16.38% was achieved. Annual heating energy consumption per unit area decreased from 333.51kWh before construction to 277.35kWh after construction, and annual cooling energy consumption per unit area decreased from 5.51kWh before construction to 5.22kWh after construction. The annual primary energy consumption per unit area decreased from 464.52kWh before construction to 403.69kWh after construction, and the annual energy cost was reduced from 3,063,307.14 won before construction to 2,641,072.49 won after construction. The additional improvement work is needed on the standards affecting energy efficiency other than insulation.

• Journal of Internet of Things and Convergence
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• v.6 no.1
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• pp.23-30
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• 2020

This paper proposes energy management directions to discover the factors of energy loss in homes and buildings and to find ways to improve them. We designed a diagnostic content app for everyday life so that not only housing experts but also the general public can use the detachable thermal imaging camera, which is a strong point of popularity and convenience. The diagnostic results using the app were analyzed using Energy Finder, an energy efficiency diagnostic solution. The survey was conducted on the senior center which is in the blind spots and facilities of the energy welfare area. The target of diagnosis was energy efficiency diagnosis by selecting 30 senior centers for over 15 years from 600 locations located in Naju City, Jeollanam-do. The overall diagnosis was good, but it is considered that the place classified as 7th grade needs to be supplemented to improve energy efficiency.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.26 no.6
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• pp.66-72
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• 2012

The main sources of energy consumption in warehouses are MHE(Material Handling Equipment), HVAC(Heating, Ventilating and Air Conditioning) and Lighting. Warehouses in advanced countries raise energy efficiencies with energy consumption diagnosis, technology development and systematic management and improvements for MHE, HVAC and Illuminating, etc. They have managed illuminating system, air conditioning, motor driven system, air circulation method, dock facility, layout, AS/RS, conveyor system and battery management, etc. Ansong Pyeongtaek area investigation resulted that 43.9[%] of enterprises are managing partly energy consumption source. But the data resulted with not substantial management of energy consumption but passive management for only electric bill curtailment. Therefore through survey research & visiting interviews of some companies in Ansong Pyeongtaek area, we understood the status of energy consumption source management and proposed energy efficiency methods on the basis of that results.

• Journal of Drive and Control
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• v.17 no.2
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• pp.38-44
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• 2020

Various efforts have been initiated to reduce the energy consumption of the compressor as it is one of the approaches to saving a large portion of the fixed cost of the production site. Various results of reducing the energy consumption of the compressor have been reported, but to reduce the energy consumption of the compressors fundamentally, regular management of the compressor should ensure optimum operation. This requires periodic on-site visits by experts, but is often overlooked as a cost issue, resulting in the use of the compressor in low-efficiency conditions. Thus, it is necessary to develop a low-cost evaluation technology for compressor condition monitoring and efficiency analysis to ensure that the compressor is always driven at the optimum efficiency without imposing undue burden on the compressor user. In this study, a sensor was installed at the inlet, outlet, and power supply of the compressor, and a method for evaluating the energy consumption of the compressor using the minimum sensor was derived. The experimental results are presented to show the validity of the proposed method. It was confirmed that the energy consumption of the compressor can be easily as well as efficiently evaluated by using the method developed in this study.

• Proceedings of the Korea Technical Association of the Pulp and Paper Industry Conference
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• 1999.04a
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• pp.45-48
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• 1999

• Journal of Drive and Control
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• v.9 no.4
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• pp.8-13
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• 2012

Recently, researches about the eco-friendly vehicles such as hybrid electric vehicle, fuel cell vehicle and electric vehicle have been actively carried out. The regenerative braking system is a key technology to improve the vehicle energy utilization efficiency because it transforms the kinetic energy to the electric energy through the electric motor. This new braking system requires cooperative control between electric controlled brake and regenerative brake. Therefore, it is necessary to establish fault-diagnosis and fail-safe evaluation criteria to secure reliability of the regenerative braking system. In this paper, the failure types and causes in regenerative braking system were analyzed. The transient behavior characteristics were examined based on fault-diagnosis and fail-safe upon failure of regenerative braking system.

• Journal of the Korean Solar Energy Society
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• v.31 no.3
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• pp.17-22
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• 2011

Techniques for mismatch loss minimization to increase the PV system efficiency are under development recently. In this paper, a method to make diagnosis of PV module mismatch is presented, which uses a concept of operating point factor. The method is based on the fact that the ratio of the incremental conductance of a PV module to instantaneous conductance is 1 when the module is operating at its maximum power point. The variations of module voltage and current are taking place by the maximum power point tracker in the power conditioning units of PV system. The effectiveness of the method is verified through an application to a real PV system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.10
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• pp.4883-4901
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• 2016

Energy efficiency in Wireless Sensor Networks (WSNs) is very appealing research area due to serious constrains on resources like storage, processing, and communication power of the sensor nodes. Due to limited capabilities of sensing nodes, such networks are composed of a large number of nodes. The higher number of nodes increases the overall performance in data collection from environment and transmission of packets among nodes. In such networks the nodes sense data and ultimately forward the information to a Base Station (BS). The main issues in WSNs revolve around energy consumption and delay in relaying of data. A lot of research work has been published in this area of achieving energy efficiency in the network. Various techniques have been proposed to divide such networks; like grid division of network, group based division, clustering, making logical layers of network, variable size clusters or groups and so on. In this paper a new technique of group based WSNs is proposed by using some features from recent published protocols i.e. "Energy-Efficient Multi-level and Distance Aware Clustering (EEMDC)" and "Energy-Efficient Multi-level and Distance Aware Clustering (EEUC)". The proposed work is not only energy-efficient but also minimizes the delay in relaying of data from the sensor nodes to BS. Simulation results show, that it outperforms LEACH protocol by 38%, EEMDC by 10% and EEUC by 13%.

• KIEAE Journal
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• v.17 no.4
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• pp.41-48
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• 2017

Purpose : Currently, 25% of the domestic energy consumption structure is used as building energy, and more than 18% of this energy is consumed in the residential. Accordingly, various efforts and policies that can save energy of the building is being performed. The various researchers are conducting research to diagnose the thermal performance of existing buildings. This study is to apply in the field of precision thermal insulation performance diagnostic method for thermal performance analysis of existing detached house in Seoul, Gangreung, Gyeongju, Pohang. And this paper is analyzed quantitatively measure the existing detached house energy performance. Method: Research methodology analyzed the thermal performance over the Heat Flow Meter method by applying the measurement process and method by applying the criteria of ISO 9869-1 & ASTR method. In this study, the surface heat transfer coefficient was calibrated by applying indoor surface heat transfer resistance with reference to ISO 6946 standard. The measurement error rate between the HFM diagnosis method and the ASTR diagnosis method was reduced and the measurement reliability was obtained through measurement method error verification. Result : As a result of the study, the thermal performance vulnerable parts of the building were quantitatively analyzed, and presented for methods which can be improved capable of efficient energy use buildings.

• Journal of Digital Convergence
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• v.9 no.2
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• pp.141-152
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• 2011

The data center has become an increasingly important part of most business operations. An increasing demand for computation has led to increasing industry energy consumption. Therefore, higher-than-normal rates of energy efficiency have become a core issue in the life cycle of data center. In this paper, we proposed the framework of level diagnosis for green data centre that can be used to diagnose the levels of capability maturity model. This framework contains the 5 key areas such as construction, air-conditioning, electricity, information technology, organization and indicators that can be applied as basic level diagnosis guide for green data center.