• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.26 no.3
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• pp.115-121
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• 2014

HVAC&R system costs can often be one of the most expensive components, representing approximately 15% of the total construction cost for office buildings. Despite their significant importance, there is a lack of a consistent and homogeneous framework to approximate the estimate research. This research deals with the prediction methodology of HVAC&R system cost with the aim of establishing a common idea for the analysis of the construction cost estimate. Our approach deals with the concept of an HVAC&R set that is composed of subsystems. The matrix combination analysis is examined, and total 960 HVAC&R system cost estimation can be implemented to large scale office buildings.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.25 no.7
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• pp.363-370
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• 2013

HVAC&R systems are the most energy consuming building services, representing approximately half of the final energy use in the building sector. Despite their significant energy use, there is a lack of a consistent and homogeneous framework to efficiently guide research. This research is about the energy consumptions of HVAC&R systems, with the aim of establishing a common idea for the analysis of building energy efficiency. Our approach deals with the concept of an HVAC&R set that is composed of subsystems. A matrix combination evaluation is examined, and a total of 960 sets can be implemented in a large scale office building. A method as a tool for energy evaluations of HVAC&R systems, and HEET as a energy simulation program, were developed.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.26 no.10
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• pp.467-473
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• 2014

Since the operating conditions of HVAC systems are different from those for which they are designed, on-going commissioning is required to optimize the energy consumed and the environment in the building. This study presents a methodology to analyze operational data and its applications. A predicted operation model is to be produced through a statistical data analysis using multiple regressions in SPSS. In this model, the dependent variable is the pre-cooling time, and the independent variables include the power output of the supply air inverter during pre-cooling, the supply air set temperature during pre-cooling, the indoor temperature-indoor set temperature just before pre-cooling, supply heat capacity, and the lowest outdoor air temperature during non-cooling/non-heating hours. The correlation coefficient R2 of the multiple regression model between the pre-cooling hour and the internal/external factors is of 0.612, and this could be used to provide information related to energy conservation and operating guidance.

• Transactions of the Korean Society of Automotive Engineers
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• v.23 no.2
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• pp.230-238
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• 2015

The main purpose of this paper is to suggest opportunities for reducing $CO_2$ emission in energy conversion of a vehicle, focused on auxiliary energy improvement in the automotive field. As part of worldwide efforts to curb global warming and to protect the domestic industry as trade barriers, many countries have set goals to regulate greenhouse gas emissions. As an example, new $CO_2$ emission regulation in EU was expected to go into effect strictly in 2020. Therefore, global car-makers need to establish strategic responsiveness of the regulations. This paper shows $CO_2$ economic value by using the correct interpretation of the relevant laws and regulations. The $CO_2$ value analyzed using quantitative figures leads to the possibility of auxiliary(accessories, HVAC, electric apparatus etc.) technology for improving fuel economy. As a result, this study generalizes the meaning of electric power saving for each driving mode by auxiliary energy improvement.