Comfort reliability evaluation of building designs by stochastic hygrothermal simulation

Sulaiman, Halimi; Olsina, Fernando

doi:10.1016/j.rser.2014.07.162

Cited by 14 publications

(4 citation statements)

References 19 publications

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“…According to Ref. [42], the most widely used comfort criterion is the one from the ASHRAE standard 55 [43]. It was developed using surveys carried out on individuals placed in controlled chambers.…”

Section: Hygric Comfortmentioning

confidence: 99%

Impact of the occupancy scenario on the hygrothermal performance of a room

Bui

Labat

Lorente

2019

Building and Environment

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Nowadays, humidity in buildings has become a major concern because it affects their energy performance, the occupants' comfort and health, and the durability of materials simultaneously. To understand the behavior of buildings towards humidity, numerical models are needed. However, the sensitivity of these models to the indoor sources, especially the ones related to the occupants' presence and activities, has not been thoroughly assessed in the literature. This article proposes to introduce a methodology to investigate the impact of the occupancy profiles of presence on the hygrothermal performance of a room. A hygrothermal model at room scale is developed and coupled with a platform called No-MASS, which provides stochastic occupancy scenarios for office buildings. Four occupancy scenarios representative of the scenarios commonly found in the literature are studied: a stochastic scenario, an average scenario derived from multiple stochastic ones, a constant scenario, and the French regulatory scenario. Comparing the results obtained with the different occupancy scenarios underlined a non-negligible impact of the scenario on indoor heat and moisture balance, mainly due to the consideration of a seasonal effect for the stochastic one.

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Section: Hygric Comfortmentioning

confidence: 99%

Impact of the occupancy scenario on the hygrothermal performance of a room

Bui

Labat

Lorente

2019

Building and Environment

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“…The randomness of this behavior, however, drives the analysis to the use of stochastic modelling. Various studies have focused on the importance of the occupants onto the building performance, especially the related to the energy consumption [8][9][10][11][12][13][14][15][16][17][18][19] and on the stochastic modelling necessary to understand the energy consumption and performance upon buildings [20][21][22][23][24][25][26][27][28][29][30]. Nonetheless, there is not, to the best of our knowledge, any similar study on Mexican buildings, neither analyzing the role of the occupants on the buildings nor developing stochastic modelling to analyze the performance of the buildings.…”

Section: Introductionmentioning

confidence: 99%

Analysis of the Energy Use in the Mexican Residential Sector by Using Two Approaches Regarding the Behavior of the Occupants

Oropeza-Perez

Petzold-Rodriguez

2018

Applied Sciences

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An analysis of the energy use in the Mexican residential sector is carried out. To achieve this, two approaches are taken into account. The first one is the usage of low-energy devices, and the second one is the decrease of their time of use. These two approaches are considered in the calculation method with random values of power and time of usage. The energy activities are divided into air-conditioning, illumination & appliances, and refrigeration. After total annual use is validated with the actual values of energy use in 2015, a sensitivity analysis of the approaches used separately and together is carried out in order to determine the potential of energy saving. Thereby, it is found that the most influential parameter for energy saving is the extensive acquisition of more efficient technologies of illumination & appliances, followed by the decrease of use of the same illumination & appliances. Furthermore, with an integrated approach that takes into account both the use of efficient devices and the reduction of their use for the three energy activities, a maximum of 19.67 TWh is calculated in 2015 for the Mexican residential sector. This approach is therefore expected to have a reliable basis for the development and improvement of policies that help to drive energy savings in an extensive manner in Mexico.

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“…However, few studies have modeled residential buildings in a probabilistic manner. Sulaiman and Olsina (2014) accounted for stochastic weather conditions by performing simulations with numerous sets of synthetically created climate data. Tian and De Wilde (2011) also used synthetic climate data, but also accounted for uncertainty in other input variables such as infiltration rate, lighting heat gain, and wall U-value in the Monte Carlo building simulations.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic modeling of the indoor climates of residential buildings using EnergyPlus

Buechler

Pallin

Boudreaux

et al. 2017

Journal of Building Physics

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The indoor air temperature and relative humidity in residential buildings significantly affect material moisture durability, heating, ventilation, and air-conditioning system performance, and occupant comfort. Therefore, indoor climate data are generally required to define boundary conditions in numerical models that evaluate envelope durability and equipment performance. However, indoor climate data obtained from field studies are influenced by weather, occupant behavior, and internal loads and are generally unrepresentative of the residential building stock. Likewise, whole-building simulation models typically neglect stochastic variables and yield deterministic results that are applicable to only a single home in a specific climate. The purpose of this study was to probabilistically model homes with the simulation engine EnergyPlus to generate indoor climate data that are widely applicable to residential buildings. Monte Carlo methods were used to perform 840,000 simulations on the Oak Ridge National Laboratory supercomputer (Titan) that accounted for stochastic variation in internal loads, air tightness, home size, and thermostat set points. The Effective Moisture Penetration Depth model was used to consider the effects of moisture buffering. The effects of location and building type on indoor climate were analyzed by evaluating six building types and 14 locations across the United States. The average monthly net indoor moisture supply values were calculated for each climate zone, and the distributions of indoor air temperature and relative humidity conditions were compared with ASHRAE 160 and EN 15026 design conditions. The indoor climate data will be incorporated into an online database tool to aid the building community in designing effective heating, ventilation, and air-conditioning systems and moisture durable building envelopes.

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Comfort reliability evaluation of building designs by stochastic hygrothermal simulation

Cited by 14 publications

References 19 publications

Impact of the occupancy scenario on the hygrothermal performance of a room

Impact of the occupancy scenario on the hygrothermal performance of a room

Analysis of the Energy Use in the Mexican Residential Sector by Using Two Approaches Regarding the Behavior of the Occupants

Probabilistic modeling of the indoor climates of residential buildings using EnergyPlus

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