Energy modeling of two office buildings with data center for green building design

Pan, Yiqun; Yin, Rongxin; Huang, Zhizhong

doi:10.1016/j.enbuild.2007.10.008

Cited by 104 publications

(37 citation statements)

References 4 publications

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“…Several academic studies on green building have emphasized technical aspects such as the application of solar thermal technology [17]; various design aspects [18,19]; marketability (e.g., people's level of willingness to purchase or fund green buildings) [20]; environmental assessment systems and tools [21,22]; and green building policies and promotions [23,24]. In contrast, issues relating to strengthening people's understanding of green buildings and environmental education have rarely been discussed.…”

Section: Introductionmentioning

confidence: 99%

Game-Based Learning for Green Building Education

Juan

Chao

2015

Sustainability

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Abstract:To improve education regarding sustainability and green buildings, we designed a multiplayer (2-4 people) green building strategy game called GBGame to deliver knowledge of and introduce applied technologies for green buildings. To verify the game's learning effect, we conducted separate measurements using the ARCS motivation model and a paper-based exam among 72 students majoring in architecture. These students were divided into an experimental group (who played the game) and a control group (who underwent lecture-based instruction). Results indicate that (1) game players had stronger learning motivation than those who attended the lectures; (2) players who learned through the game generally exhibited better learning effectiveness than non-players; (3) following cross-validation, we discovered a highly significant correlation between the learning effect and players' attention, perception of relevance, confidence, and satisfaction during the game; and (4) the game system was found to be attractive to the students, implying that it can be an effective instructional and recreational material to use during green building education.

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Section: Introductionmentioning

confidence: 99%

Game-Based Learning for Green Building Education

Juan

Chao

2015

Sustainability

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“…Therefore, energy models are difficult to calibrate for non-working hours without considering overtime as an important input. In current building simulations, the internal heat gains and HVAC operation schedules are usually deterministic schedules based on a typical weekday, weekend or holiday, either from measurement or design practice [22][23][24]. These schedules do not realistically represent overtime schedules due to their stochastic nature, and because of simplistic and idealistic data inputs that are unrepresentative of actual occupancy [25].…”

Section: Introductionmentioning

confidence: 99%

Stochastic modeling of overtime occupancy and its application in building energy simulation and calibration

Sun

Yan

Hong

et al. 2014

Building and Environment

103

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Overtime is a common phenomenon around the world. Overtime drives both internal heat gains from occupants, lighting and plug-loads, and HVAC operation during overtime periods. Overtime leads to longer occupancy hours and extended operation of building services systems beyond normal working hours, thus overtime impacts total building energy use. Current literature lacks methods to model overtime occupancy because overtime is stochastic in nature and varies by individual occupants and by time. To address this gap in the literature, this study aims to develop a new stochastic model based on the statistical analysis of measured overtime occupancy data from an office building. A binomial distribution is used to represent the total number of occupants working overtime, while an exponential distribution is used to represent the duration of overtime periods. The overtime model is used to generate overtime occupancy schedules as an input to the energy model of a second office building. The measured and simulated cooling energy use during the overtime period is compared in order to validate the overtime model. A hybrid approach to energy model calibration is proposed and tested, which combines ASHRAE Guideline 14 for the calibration of the energy model during normal working hours, and a proposed KS test for the calibration of the energy model during overtime. The developed stochastic overtime model and the hybrid calibration approach can be used in building energy simulations to improve the accuracy of results, and better understand the characteristics of overtime in office buildings.

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“…Empirical validation has been demonstrated in many field studies (Pan et al, 2008;Raftery et.al, 2011;Yin et al, 2012;Wang et.al, 2013;O'Neill et.al, 2013). Among the milestones in model calibration was the development of a systematic method using a "base load analysis approach" (Yoon et al, 2003), which uses a combination of monthly utility billing data and sub-metered data to calibrate a building energy performance model.…”

Section: List Of Tablesmentioning

confidence: 99%

“…In that study, building system loads were characterized as "weather dependent" (HVAC system loads) and "weather independent" (e.g., lighting and plug loads). Pan et al (2008) calibrated a simulation model in a high-rise commercial building using a step-by-step method based on the approach proposed in American Society of Heating, Refrigerating, and Air Conditioning Engineers (ASHRAE) Guideline 14-2002. In 1994, Norford et al presented a commonsense procedure for calibrating a DOE-2 computer model of a commercial building, identifying the major building loads, including lighting and equipment.…”

Section: List Of Tablesmentioning

confidence: 99%

Linking measurements and models in commercial buildings: A case study for model calibration and demand response strategy evaluation

Yin

Kiliccote

Piette

2016

Energy and Buildings

Self Cite

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The use of simulation to evaluate energy-efficient operations, commissioning problems, and demandresponse (DR) strategies offers important insights into building operations. This paper describes a stepby-step procedure for using measured end-use energy data from a campus building to calibrate a simulation model developed in EnergyPlus. This process included identification of key input parameters for reducing uncertainties in the model. The building geometry and internal thermal zones were modeled to match the actual heating ventilation and air conditioning (HVAC) zoning for each individual variable air-volume (VAV) zone. We evaluated most key building and HVAC system components, including space loads (actual occupancy number, lighting and plug loads), HVAC air-side components (VAV terminals, supply and return fans) and water-side components (chillers, pumps, and cooling towers).Comparison of the pre-and post-calibration model shows that the calibration process greatly improves the model's accuracy for each end use. We propose an automated model calibration procedure that links the model to a real-time data monitoring system, allowing the model to be updated any time. The approach enables the automated data feed from sMAP into the EnergyPlus model to create realistic schedules of space loads (occupancy, lighting and plug), performance curves of fans, chillers and cooling towers. We also field-tested DR control strategies to evaluate the model's performance in predicting dynamic response effects. Finally, this paper describes application of the calibrated model to analyze control systems and DR strategies with the goal of reducing peak demand. We compare end-use data from modeled and actual DR events.

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Energy modeling of two office buildings with data center for green building design

Cited by 104 publications

References 4 publications

Game-Based Learning for Green Building Education

Game-Based Learning for Green Building Education

Stochastic modeling of overtime occupancy and its application in building energy simulation and calibration

Linking measurements and models in commercial buildings: A case study for model calibration and demand response strategy evaluation

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