IBPSA2011

Energy and indoor environmental performance of buildings are highly influenced by outdoor/indoor climate, by building characteristics, and by occupants’ behaviour. Building simulation tools cannot precisely replicate the actual performance of buildings because the simulations are based on a number of basic assumptions that affect the results. Therefore, the calculated energy performance may differ significantly from the real energy consumption.

In order to decrease the concentration of indoor pollutants, sorptive building materials have been used. Giving the construction materials themselves the property of reducing the concentration of indoor pollutants has been reported as simple and effective, because it is highly effective without requiring the operation of special equipment. Concentration reduction performance of indoor air pollutants by sorptive building materials depend significantly on air exchange rate, loading factor, mass transfer coefficients etc.

It is important to insulate the glazing or frames of windows efficiently because they usually contribute to some of the greatest heat loss from dwelling houses. To improve window insulation, we propose a new dynamic system applied to window frames. This system is composed of three parts: a dynamic insulation system applied to window frames, a mechanical ventilation system, and a heat-recovery heat pump system. In order to confirm its feasibility, we evaluated various insulation patterns, materials and structures, by computational fluid dynamics. 

We designed a new air supply window system to ventilate indoors through the air space of a double pane window and evaluated its insulation efficiency and probability of moisture condensation in order to confirm its feasibility and applicability. Then, to verify its thermal insulation efficiency, we evaluated its temperature contribution with different air space widths using computational fluid dynamics.

The objective of the work is to identify the types of occupant-driven residential behaviour variations that most significantly impact a designer’s ability to predict energy consumption and peak electrical demand of a house. The study compares the sensitivities of results for a typical house and compares with a house designed to achieve net zero energy consumption, where occupant-driven loads are more influential.

Evaporative cooling is an attractive energy-efficient technique for producing a comfortable indoor environment. The efficiency and low cost of water spray evaporative cooling systems makes them a good alternative to reduce energy use.

In future highly energy-efficient buildings, heat pumps will play a key role. Hence, annual efficiency calculation and optimization by means of simulating heat pump heating and cooling systems are very val-uable, especially if building and building technology are coupled. 

The aim of the work is to present a new methodology that allows to identify the most important parameters affecting the energy performance of buildings under certain conditions. The methodology consists of analysing the different contributions to the convective energy balance on internal air: each contribution is split according the dynamic driving forces of outdoor and indoor environment.   The paper describes the developed procedure which consists of a set of numerical simulations using EnergyPlus.

Many simulation software to predict thermal environment of buildings, such as temperature, humidity, heating and cooling load of building spaces, have been developed. However, most of them do not take into account moisture transfer in wall assemblies. Humidity calculation in most software is simply affected by ventilation and focuses on just the building spaces. Then, sensory index such as standard new effective temperature is even excluded from calculation.

In this paper we present a method to simulate res-idential building occupants’ activities, which can be directly used to predict occupants’ presence and as an input to models of occupants’ behavior, resulting in more coherent and accurate predictions of build-ings’ energy demands for heating, ventilating and air-conditioning as well as for lighting and electrical ap-pliances. First we describe a stochastic model of the activity chains of residential building occupants and the calibration of this model using French time-use survey data (for the period 1998/1999).