Hybrid probabilistic-possibilistic treatment of uncertainty in building energy models : a case study of sizing peak cooling loads - CentraleSupélec Access content directly
Journal Articles ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering Year : 2018

Hybrid probabilistic-possibilistic treatment of uncertainty in building energy models : a case study of sizing peak cooling loads

Fazel Khayatian
  • Function : Author
  • PersonId : 1051422
Maryam Meshkinkiya
  • Function : Author
  • PersonId : 1051423
Piero Baraldi
  • Function : Author
  • PersonId : 905823
Francesco Di Maio
  • Function : Author
  • PersonId : 968420
Enrico Zio

Abstract

Optimal sizing of peak loads has proven to be an important factor affecting the overall energy consumption of heating ventilation and air-conditioning (HVAC) systems. Uncertainty quantification of peak loads enables optimal configuration of the system by opting for a suitable size factor. However, the representation of uncertainty in HVAC sizing has been limited to probabilistic analysis and scenario-based cases, which may limit and bias the results. This study provides a framework for uncertainty representation in building energy modeling, due to both random factors and imprecise knowledge. The framework is shown by a numerical case study of sizing cooling loads, in which uncertain climatic data are represented by probability distributions and human-driven activities are described by possibility distributions. Cooling loads obtained from the hybrid probabilistic-possibilistic propagation of uncertainty are compared to those obtained by pure probabilistic and pure possibilistic approaches. Results indicate that a pure possibilistic representation may not provide detailed information on the peak cooling loads, whereas a pure probabilistic approach may underestimate the effect of uncertain human behavior. The proposed hybrid representation and propagation of uncertainty in this paper can overcome these issues by proper handling of both random and limited data.
Fichier principal
Vignette du fichier
2018_Hybrid-Probabilistic-Possibilistic-Treatment-of-Uncertainty-in-Building-Energy-Models.pdf (737.22 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-02194936 , version 1 (19-03-2020)

Identifiers

Cite

Fazel Khayatian, Maryam Meshkinkiya, Piero Baraldi, Francesco Di Maio, Enrico Zio. Hybrid probabilistic-possibilistic treatment of uncertainty in building energy models : a case study of sizing peak cooling loads. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 2018, 4 (4), ⟨10.1115/1.4039784⟩. ⟨hal-02194936⟩
47 View
152 Download

Altmetric

Share

Gmail Facebook X LinkedIn More