Ground reflectivity in the context of building energy simulation

Thévenard, Didier; Haddad, Kamel

doi:10.1016/j.enbuild.2005.11.007

Cited by 54 publications

(28 citation statements)

References 10 publications

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“…HE can be calculated via equation (15), and heat storage can be described by the room air temperature response equation of equation (16). If the corresponding heat storage coefficient can be obtained, according to equations (15) and (16), the room thermal load soft sensing can be realized as follows…”

Section: Room Heat Balance Equationmentioning

confidence: 99%

“…10,11 The software developed quickly in the last couple of years to predict building energy consumption, including DOE-2, 12 ESP-r, 13 EnergyPlus, 14 and DeST. 15 Although different kinds of simulation software can accurately predict the building thermal load in many projects, [16][17][18] the simulation results differ in the prediction of occupied buildings' energy consumption. 19 Because of various parameters setting, the building energy simulation software is too complicated and time-consuming.…”

Section: Introductionmentioning

confidence: 99%

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Comprehensive approach to modeling and simulation of dynamic soft-sensing design for real-time building energy consumption

Liu

Fang

et al. 2017

International Journal of Distributed Sensor Networks

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This research develops a multi-layer hybrid soft-sensor model to improve the accuracy of building thermal load prediction using integrated data. The multi-layer hybrid model (autoregressive and particle swarm optimization neural network) hybridizes an autoregressive model with exogenous inputs and a particle swarm optimization neural network. The distributed sensors' experimental scenario was set in a medium-sized office building located in Shanghai, which has applied this multi-layer hybrid model to evaluate the prediction accuracy, meanwhile its performance was also compared with several commonly used methods under different evaluation criteria. Through frequency-domain decomposition, the heat balance equation is used to validate the autoregressive and particle swarm optimization neural network model. Both the simulation of building thermal load and experiment results demonstrate that the proposed autoregressive and particle swarm optimization neural network method can recognize soft sensing of the building thermal load much more quickly and efficiently, and achieve higher accuracy in both cooling load and heating load prediction. KeywordsAutoregressive model with exogenous inputs, particle swarm optimization neural network, multi-layer hybrid model (autoregressive and particle swarm optimization neural network), soft sensor, thermal load Date

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Section: Room Heat Balance Equationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Comprehensive approach to modeling and simulation of dynamic soft-sensing design for real-time building energy consumption

Liu

Fang

et al. 2017

International Journal of Distributed Sensor Networks

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“…For this simulation, the ground reflectance is assumed to be 0.2, which is the commonly used value in the building energy simulations [16]. The angle of incidence is defined as…”

Section: Definition Of Radiation Incident On Collector Surfacementioning

confidence: 99%

Transient Uncertainty Analysis in Solar Thermal System Modeling

Cho

Smith

Luck

et al. 2017

J. Uncertain. Anal. Appl.

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Complex, dynamic, computational models are routinely used to evaluate and optimize the design and performance of solar thermal systems. As models become more complex, performing uncertainty analysis on such models can be quite challenging and computationally expensive. This paper presents an effective approach to quantify uncertainties associated with transient simulation results from a dynamic solar thermal energy system model with uncertain parameters. The proposed method utilizes the concept of impulse response and convolution process to estimate the sensitivities to time-varying external inputs. Using this method, the number of simulations required to propagate uncertainties through dynamic models can be significantly reduced. An example is presented throughout the paper to demonstrate the procedure of the proposed uncertainty analysis approach.

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“…Similarly, to fully account for the inaccuracy of convection heat transfer, Palyvos [8] have summarized and provided a critical discussion on all the available correlations of external wind coefficients. Also, Thevenard and Haddad [9] have compared results from two building simulation models, in which one of them used typical reflectance for meteorological years while the other use actual recorded data. The results show that the difference could be up to 10.9% on yearly basis while 23.3% on a monthly basis.…”

Section: Literature Reviewmentioning

confidence: 99%

Hybrid Ventilation Potential Investigation for Small and Medium Office Building in Different US Climates Under Uncertainties

Chen¹,

Augenbroe²,

Song³

2017

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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Hybrid ventilation, or mixed mode ventilation, is a technique that uses a mixture of natural and mechanical ventilation for maintaining a thermally comfortable indoor environment for building occupants. With its potential for improving both the energy efficiency and occupants comfort at the same time, its real application benefits across different US climate zones are still not clear, especially considering the possible variations of building context and building properties in real scenarios. In this paper, by taking these uncertainties into account, we have investigated the potential of utilizing hybrid ventilation on small to medium size commercial buildings across different US climate zones. One prototypical small to medium size office building was selected first. Uncertainties related to meteorology, building microclimate, building properties and operation have been applied for a thorough investigation of hybrid ventilation potential based on our baseline building. A rule based control has been specified based on the adaptive thermal comfort model in the thermal comfort standard. The results showed that the mean energy saving varies from 17% to 43.8% in different climate zones in US.

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Ground reflectivity in the context of building energy simulation

Cited by 54 publications

References 10 publications

Comprehensive approach to modeling and simulation of dynamic soft-sensing design for real-time building energy consumption

Comprehensive approach to modeling and simulation of dynamic soft-sensing design for real-time building energy consumption

Transient Uncertainty Analysis in Solar Thermal System Modeling

Hybrid Ventilation Potential Investigation for Small and Medium Office Building in Different US Climates Under Uncertainties

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