Exploring HVAC system sizing under uncertainty

Sun, Yuming; Gu, Li; Wu, Changbao; Augenbroe, Godfried

doi:10.1016/j.enbuild.2014.06.026

Cited by 115 publications

(60 citation statements)

References 23 publications

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“…3. This distribution of predicted peak cooling load is taken into account in the decision making instead of only one deterministic value, which is similar to the sizing approach proposed by Sun et al [11]. In the framework of statistics, the HVAC system will be sized by selecting a capacity Q to satisfy…”

Section: Capacity Selection Of the Designmentioning

confidence: 99%

“…To study the influence of uncertainties on the design of HVAC systems, Sun et al [11] proposed a new framework to guide the use of uncertainty analysis (UA) and sensitivity analysis (SA) in HVAC system sizing, in which UA replaces the safety factor with quantified margins based on comprehensive quantification of different sources of uncertainty and SA is then used to identify the important individual factors or groups of factors that contribute to uncertainty. Although this work provided a mean of risk management by applying better quality assurance methods, it did not analyze the potential influence of uncertainties on the performance of the design, for example energy performance and operation cost were not deeply investigated.…”

Section: Introductionmentioning

confidence: 99%

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HVAC system design under peak load prediction uncertainty using multiple-criterion decision making technique

Huang

Wang

2015

Energy and Buildings

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Section: Capacity Selection Of the Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

HVAC system design under peak load prediction uncertainty using multiple-criterion decision making technique

Huang

Wang

2015

Energy and Buildings

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“…In this paper, we apply a variancebased sensitivity analysis methodology, where a first-order or main-effect sensitivity index represents the expected reduction in the percentage of output variance, if the parameter under investigation could be known or fixed. Sensitivity indexes of uncertain parameters are computed with the techniques of Sun et al [46]. The sensitivity analysis technique is premised on Latin hypercube samples from the previous uncertainty analysis and includes two steps: (1) use lasso [47] as a screening procedure to remove insignificant parameters, (2) compute global sensitivity indexes in percentage with a variance-based method.…”

Section: Sensitivity Analysis Techniquementioning

confidence: 99%

Meta-modeling of occupancy variables and analysis of their impact on energy outcomes of office buildings

et al. 2016

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h i g h l i g h t sA meta-analysis framework for a stochastic characterization of occupancy variables. Sensitivity ranking of occupancy variability against all other sources of uncertainty. Sensitivity of occupant presence for building energy consumption is low. Accurate mean knowledge is sufficient for predicting building energy consumption. Prediction of peak demand behavior requires stochastic occupancy modeling. a b s t r a c tOccupants interact with buildings in various ways via their presence (passive effects) and control actions (active effects). Therefore, understanding the influence of occupants is essential if we are to evaluate the performance of a building. In this paper, we model the mean profiles and variability of occupancy variables (presence and actions) separately. We will use a multi-variate Gaussian distribution to generate mean profiles of occupancy variables, while the variability will be represented by a multi-dimensional time series model, within a framework for a meta-analysis that synthesizes occupancy data gathered from a pool of buildings. We then discuss variants of occupancy models with respect to various outcomes of interest such as HVAC energy consumption and peak demand behavior via a sensitivity analysis. Results show that our approach is able to generate stochastic occupancy profiles, requiring minimum additional input from the energy modeler other than standard diversity profiles. Along with the metaanalysis, we enable the generalization of previous research results and statistical inferences to choose occupancy variables for future buildings. The sensitivity analysis shows that for aggregated building energy consumption, occupant presence has a smaller impact compared to lighting and appliance usage. Specifically, being accumulatively 55% wrong with regard to presence, only translates to 2% error in aggregated cooling energy in July and 3.6% error in heating energy in January. Such a finding redirects focus to the accurate estimation of lighting and appliance usage for a better prediction of aggregated energy consumption. Furthermore, it proves that accurate knowledge of the mean profiles is sufficient, that is, stochastic occupancy models do not play a significant role in the prediction of aggregated consumption in a conventional office building where the interaction between the operation of building systems and the spatial and temporal variability of occupancy is weak. When it comes to peak demand behavior, occupancy variability should be taken into account, as static profiles are not able to produce adequate estimates of power duration probabilities close to the power peak.

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“…The prediction of building energy consumption for a large number of buildings requires relying on different procedures to (1) build large-scale building energy models, (2) include the influence of the urban neighborhood in the energy models, (3) include on-site data from the energy audits into the building energy models, (4) perform sensitivity analysis on the uncertainties associated with the inputs of the building energy models [95][96][97], and (5) calibrate the M A N U S C R I P T…”

Section: Overview Of the Energy And Airflow Modeling For Neighborhoodsmentioning

confidence: 99%

Building neighborhood emerging properties and their impacts on multi-scale modeling of building energy and airflows

Srebric

Heidarinejad

Liu

2015

Building and Environment

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Exploring HVAC system sizing under uncertainty

Cited by 115 publications

References 23 publications

HVAC system design under peak load prediction uncertainty using multiple-criterion decision making technique

HVAC system design under peak load prediction uncertainty using multiple-criterion decision making technique

Meta-modeling of occupancy variables and analysis of their impact on energy outcomes of office buildings

Building neighborhood emerging properties and their impacts on multi-scale modeling of building energy and airflows

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