Probabilistic energy consumption analysis in buildings using point estimate method Mohammad Javad Bordbari, Ali Reza Seifi, Mohammad Rastegar * Department of Power and Control, School of Electrical and Computer Engineering, Shiraz University, Zand St., Shiraz, Iran article info Article history: Received 10 June 2017 Received in revised form 5 October 2017 Accepted 20 October 2017 Keywords: Energy efficiency Energy consumption analysis Energy cost Thermal comfort Two-point estimate method abstract This paper analyzes the energy consumption of buildings considering the uncertainty of structural and environmental parameters. To this end, two-point estimate method (2PEM) is used to model the un- certainties. EnergyPlus software is used in this paper to evaluate the energy consumption, the thermal comfort, and the energy cost of a building. We examine the proposed method in a retail building to show the effectiveness of the method in comparison with the Monte-Carlo Simulation and deterministic methods. The results show that the 2PEM method although may cause a bit accuracy loss, it can considerably reduce the simulation time by more than 97%. In addition, a sensitivity analysis is effec- tuated in this paper to investigate the impacts of different climate zones on the results of energy con- sumption analysis. © 2017 Elsevier Ltd. All rights reserved. 1. Introduction The high incremental rate of energy consumption and lack of fossil fuels, as the main source of energy, may lead to a life- threatening event in upcoming years. According to forecasts of International Energy Outlook organization [1], the rate of world energy consumption will increase about 45% from 2017 to 2040. In addition, if the consumption of fossil fuels is going on with the current rate, these resources will end by 2030 [2]. Energy efficiency analysis (EEA) has been recently proposed as a promising solution to mitigate the criticality of energy consumption [3]. This analysis makes the energy consumption more efficient by minimizing the energy losses on the consumers' side. Since around 40% of the worldwide energy is consumed in the buildings, i.e., residential and commercial demands [1,4] these buildings can play an important role in the EEA procedure. In addition, the average energy consumption of buildings grows 2.2%/ year, which is more than other sectors. It makes the role of build- ings more important in the energy analysis. The first step in EEA of buildings is energy consumption analysis (ECA), as studied in Refs. [5e20]. ECA calculates the energy consumption of a building using different structural and thermal parameters to find a solution for the loss reduction. Recently, this calculation is done in the strong simulation-based programs such as Energy Plus and BLAST [21e26]. As stated in Refs. [27e30], a part of considered parameters in ECA analysis depends on consumers' daily habit and thermal conditions. It adds some amount of un- certainty to the analysis, which makes ECA more complex. Few papers are available in the technical literature considering the uncertainty in the ECA of buildings [21e23]. In Ref. [21], Monte Carlo Simulation (MCS) method is used to consider the uncertainty of 6 parameters, such as cooling set point, equipment density, fan efficiency and Coil cooling cop, in the evaluation of the consumed energy in a building. Other parameters, such as infiltration rate, people density, and U-Value roof are assumed fixed in the study. Thus, curve-fitting method is used to predict energy consumption according to the results of applying MCS. In Ref. [22], the Monte Carlo Simulation (MCS) method is used to generate a set of regression equations to evaluate the energy consumption. The MCS method uses numerous random values, extracted from the proba- bility distribution function (PDF) of the uncertain parameters to calculate the PDF of the consumed energy. Although MCS is the simplest way to model uncertainties, it would have computational complexities and be time-consuming, if the number of uncertain parameters increased [31]. For this reason, the sensitivity indices and Meta-model is used in Ref. [23] to decrease the number of uncertain parameters. In addition, in Ref. [21], first, a sensitivity * Corresponding author. E-mail addresses: m.j.bordbari@shirazu.ac.ir (M.J. Bordbari), seifi@shirazu.ac.ir (A.R. Seifi), mohammadrastegar@shirazu.ac.ir (M. Rastegar). Contents lists available at ScienceDirect Energy journal homepage: www.elsevier.com/locate/energy https://doi.org/10.1016/j.energy.2017.10.091 0360-5442/© 2017 Elsevier Ltd. All rights reserved. Energy 142 (2018) 716e722