EnergyPlus: creating a new-generation building energy simulation program

Crawley, Drury B.; Lawrie, Linda K.; Winkelmann, F.C.; Buhl, W.F.; Huang, Yu; Pedersen, C.O.; Strand, Richard Karl; Liesen, Richard J.; Fisher, Daniel E.; Witte, Michael J.; Glazer, Jason

doi:10.1016/s0378-7788(00)00114-6

Cited by 2,109 publications

(894 citation statements)

References 4 publications

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“…There exist a number of residential simulation models that calculate energy demand by solving a series of coupled time-dependent equations representing the physical processes of heat (and sometimes moisture) transfer (Florida Solar Energy Center 2012;Fairey, Vieira, and Parker 2000;Halrecht, Zmeureanu, and BeausoleilMorrison 1999;RESNET 2014;Crawley et al 2001). The models can account for radiative and convective heat transfer at the various surfaces of the building envelope (including solar gains); conduction through walls, floors, and ceilings; heat transfer to and from the attic; convective transfer with airflow, heat, or enthalpy addition/removal by HVAC equipment; and internal heat generation (Figure 1a).…”

Section: Rationalementioning

confidence: 99%

“…However, these software packages do not have very sophisticated ventilation models. EnergyPlus (Crawley et al 2001), developed by the US Department of Energy (DOE) is used for both commercial and residential building energy simulations, but has been shown to have deficiencies in modelling infiltration impacts in residential modelling (Spencer 2010).…”

Section: The Regcap Modelling Approachmentioning

confidence: 99%

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A simplified model for estimating population-scale energy impacts of building envelope air tightening and mechanical ventilation retrofits

Logue

Turner

Walker

et al. 2015

Journal of Building Performance Simulation

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Changing the air exchange rate of a home (the sum of the infiltration and mechanical ventilation airflow rates) affects the annual thermal conditioning energy. Large-scale changes to air exchange rates of the housing stock can significantly alter the residential sector's energy consumption. However, the complexity of existing residential energy models is a barrier to the accurate quantification of the impact of policy changes on a state or national level.The Incremental Ventilation Energy (IVE) model developed in this study combines the output of simple air exchange models with a limited set of housing characteristics to estimate the associated change in energy demand of homes. The IVE model was designed specifically to enable modellers to use existing databases of housing characteristics to determine the impact of ventilation policy change on a population scale. The IVE model estimates of energy change when applied to US homes with limited parameterisation are shown to be comparable to the estimates of a wellvalidated, complex residential energy model.

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Section: Rationalementioning

confidence: 99%

Section: The Regcap Modelling Approachmentioning

confidence: 99%

A simplified model for estimating population-scale energy impacts of building envelope air tightening and mechanical ventilation retrofits

Logue

Turner

Walker

et al. 2015

Journal of Building Performance Simulation

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“…First, one can develop a physics-based model using simulation software such as EnergyPlus [4] and TRNSYS [5]. A precise model is obtained this way, but it is time consuming and expensive to develop and requires immense computational resources.…”

Section: Introductionmentioning

confidence: 99%

“…This type of the methods is computationally efficient for solving largescale problems since their state space can be significantly reduced by the events defined by a set of state transitions. In addition, many other methods, such as genetic algorithms [7], [18], fuzzy control [16]- [17], simulation-based optimization methods [4]- [5], etc., were attempted. The above literature showed that 10%-30% of energy consumption (costs) can be reduced by the optimization of building operation.…”

Section: Introductionmentioning

confidence: 99%

PMV-based event-triggered mechanism for building energy management under uncertainties

Spanos

et al. 2017

Energy and Buildings

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This paper provides a study of the optimal scheduling of building operation to minimize its energy cost under building operation uncertainties. Opposed to the usual way that describes thermal comfort using a static range of air temperature, the optimization of a tradeoff between energy cost and thermal comfort predicted mean vote (PMV) index is addressed in this paper. In order to integrate the calculation of the PMV index with the optimization procedure, we develop a sufficiently accurate approximation of the original PMV model which is computationally efficient. We develop a model-based periodic eventtriggered mechanism (ETM) to handle the uncertainties in the building operation. Upon the triggering of predefined events, the ETM determines whether the optimal strategy should be recalculated. In this way, the communication and computational resources required can be significantly reduced. Numerical results show that the ETM method is robust with respect to the uncertainties in prediction errors and results in a reduction of more than 60% in computation without perceivable degradation in system performance as compared to a typical closed-loop model predictive control.

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“…Here, 2 sets of 1000 samples are used to estimate the sensitivity indices (2) and (10), according to the estimator provided in [32]. Then, the model is simulated with the generated static and dynamic inputs, using the dedicated software EnergyPlus [33]. Each simulation lasts 1 minute.…”

mentioning

confidence: 99%

Sensitivity analysis of complex models: Coping with dynamic and static inputs

Anstett-Collin

Goffart

Mara³

et al. 2015

Reliability Engineering & System Safety

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. Sensitivity analysis of complex models: coping with dynamic and static inputs. Reliability Engineering and System Safety, Elsevier, 2015, 134, pp.268-275 AbstractIn this article, we address the issue of conducting a sensitivity analysis of complex models with both static and dynamic uncertain inputs. While several approaches have been proposed to compute the sensitivity indices of the static inputs (i.e. parameters), the ones of the dynamic inputs (i.e. stochastic fields) have been rarely addressed. For this purpose, we first treat each dynamic as a Gaussian process. Then, the truncated Karhunen-Loève expansion of each dynamic input is performed. Such an expansion allows to generate independent Gaussian processes from a finite number of independent random variables. Given that a dynamic input is represented by a finite number of random variables, its variance-based sensitivity index is defined by the sensitivity index of this group of variables. Besides, an efficient samplingbased strategy is described to estimate the first-order indices of all the input factors by only using two input samples. The approach is applied to a buildPreprint submitted to Reliability Engineering and System Safety August 5, 2014ing energy model, in order to assess the impact of the uncertainties of the material properties (static inputs) and the weather data (dynamic inputs) on the energy performance of a real low energy consumption house.

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EnergyPlus: creating a new-generation building energy simulation program

Cited by 2,109 publications

References 4 publications

A simplified model for estimating population-scale energy impacts of building envelope air tightening and mechanical ventilation retrofits

A simplified model for estimating population-scale energy impacts of building envelope air tightening and mechanical ventilation retrofits

PMV-based event-triggered mechanism for building energy management under uncertainties

Sensitivity analysis of complex models: Coping with dynamic and static inputs

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