Natural ventilation in residential building archetypes: a stochastic approach based on occupant behaviour and thermal comfort

Files in This Item:
File Description SizeFormat 
Natural_Ventilation_in_Residential_Buildings.pdf426.72 kBAdobe PDFDownload
Title: Natural ventilation in residential building archetypes: a stochastic approach based on occupant behaviour and thermal comfort
Authors: Neu, Olivier
Evon, Valentin
Oxizidis, Simeon
Flynn, Damian
Finn, Donal
Permanent link: http://hdl.handle.net/10197/8135
Date: 10-May-2014
Abstract: As houses become more energy efficient due to highly thermal resistant fabrics, the impact of natural ventilation on indoor comfort and on transient heating and cooling loads increases. These two constraints must be integrated within building performance simulation models when assessing the potential for electrical load shifting strategies in residential buildings placed in a smart grid environment. A natural ventilation model is developed and implemented for five residential building archetypes. A bottom-up methodology based on occupant behaviour, through the use of time-of-use data, is implemented at room level within EnergyPlus. A stochastic approach determines whether to open or close windows, depending on the occupancy state, the activity type and level, and the thermal comfort experienced. The algorithms proposed consider the main drivers governing window operation within a residential context. Focus is placed on the modelling challenges, and the impacts of the model are assessed using energy performance and thermal comfort.
Funding Details: Science Foundation Ireland
Type of material: Conference Publication
Publisher: International Building Performance Simulation Association (IBPSA)
Keywords: Energy models;Building energy simulation;Residential sector;Ireland
Language: en
Status of Item: Not peer reviewed
Conference Details: IBPSA-Canada eSim Conference: Removing barriers to application of Building Performance Simulation in design practice, Ottawa, Canada, 07-10 May 2014
Appears in Collections:Mechanical & Materials Engineering Research Collection
ERC Research Collection
Electrical and Electronic Engineering Research Collection

Show full item record

Download(s) 50

54
checked on May 25, 2018

Google ScholarTM

Check


This item is available under the Attribution-NonCommercial-NoDerivs 3.0 Ireland. No item may be reproduced for commercial purposes. For other possible restrictions on use please refer to the publisher's URL where this is made available, or to notes contained in the item itself. Other terms may apply.