• International conference on construction engineering and project management
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• 2015.10a
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• pp.523-526
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• 2015

In recent years, building commissioning has often been part of a standard delivery practice in construction, particularly in the high-performance green building market, to ensure the building is designed and constructed per owner's requirements. Commissioning, therefore, intends to provide quality assurance that buildings perform as intended by the design and often helps achieve energy savings. Commissioning, however, is not as widely adopted as its potential benefits are perceived. Owners are still skeptical of the cost-effectiveness claims by energy management and commissioning professionals. One of the issues in the current commissioning practice is that not every project is guaranteed to benefit from the commissioning services. This, coupled with its added cost, the commissioning service is not acquired with great acceptance and confidence by building owners. To overcome this issue, this paper presents a unique methodology to enhance owner's predicting capability of the degree of success of commissioning service using the Bayesian theorem. The paper analyzes a situation where a future building owner wants to use a pre-commissioning in an attempt to refine the success rate of the future commissioned building performance. The author proposes the Bayesian theorem based framework to improve the current commissioning practice where building owners are not given accurate information how much successful their projects are going to be in terms of energy savings from the commissioning service. What should be provided to the building owners who consider their buildings to be commissioned is that they need some indicators how likely their projects benefit from the commissioning process. Based on this, the owners can make better informed decisions whether or not they acquire a commissioning service.

• 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.

• Land and Housing Review
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• v.15 no.2
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• pp.29-37
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• 2024

This study investigates the thermal conductivity and density of expanded polystyrene insulation materials collected from buildings under going energy retrofit projects. Due to the absence of initial thermal conductivity data, determining precise long-term patterns was challenging. Analysis based on design documents revealed that expanded polystyrene insulation maintained consistent performance over ten years. Notably, the thermal conductivity measurements of insulation samples of the same grade and age varied significantly. Additionally, the insulation density was found to be substantially below the standard specified in the design documents. The results of the experiment indicate that performance management during both construction and operation phases is lacking. It is crucial to apply building commissioning, which involves performance verification throughout the building's life cycle, to properly evaluate building energy performance improvements, such as building energy retrofit projects.

• Journal of the Korean Society of Systems Engineering
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• v.14 no.1
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• pp.13-18
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• 2018

Plant Engineering is a series of activities going through following life cycle phases: planning, basic design, detailed design, procurement and construction, commissioning, operation and maintenance, to produce a target product. From among these phases of plant engineering life cycle, a detailed design phase is an important phase producing final design deliveries. Luckily, through technical co-operation and experiences of constructing plants, large Korean engineering companies have accumulated know-hows of efficient detailed designs. However, smaller engineering companies have less experience of performing detailed designs so there is always a risk of causing design errors in the detailed design phase. To mitigate the risk of design errors in the detailed design phase, it is necessary to systematize a concrete activity model of a detailed design phase. In this paper, we have developed a prototype of a detailed design activity model through a widely used function modeling methodology called IDEF0.