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Hybrid Probabilistic-Possibilistic Treatment of Uncertainty in Building Energy Models: A Case Study of Sizing Peak Cooling Loads

[+] Author and Article Information
Fazel Khayatian

Department of Architecture Built Environment and Construction Engineering, Politecnico di Milano, Via Ponzio, 31, 20133 Milano, Italy
fazel.khayatian@polimi.it

Maryam MeshkinKiya

Department of Architecture Built Environment and Construction Engineering, Politecnico di Milano, Via Ponzio, 31, 20133 Milano, Italy
maryam.meshkinkiya@polimi.it

Piero Baraldi

Department of Energy, Politecnico di Milano, Via Ponzio, 34/3, 20133 Milano, Italy
piero.baraldi@polimi.it

Francesco Di Maio

Department of Energy, Politecnico di Milano, Via Ponzio, 34/3, 20133 Milano, Italy
francesco.dimaio@polimi.it

Enrico Zio

Chaire Systems Science and the Energy Challenge, Fondation Electricite’ de France, Laboratoire Genie Industriel, CentraleSupélec/Université Paris-Saclay, Grande voie des Vignes, 92290 Chatenay-Malabry, FranceDepartment of Energy, Politecnico di Milano, Via Ponzio, 34/3, 20133 Milano, Italy
enrico.zio@polimi.it

1Corresponding author.

ASME doi:10.1115/1.4039784 History: Received September 05, 2017; Revised March 20, 2018

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 is 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.

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