IBPSA2009

The main objective of this article is to present the calibration process of a computer model of a naturally-ventilated house built in southern Brazil. The house was monitored over two seven-day periods by using Hobo data loggers. The EnergyPlus computer program was used to create a computer model for the house; parameters related to air infiltration and natural ventilation were modeled by using the AirflowNetwork. The internal air temperatures obtained from the simulations were compared with those measured in the house.

This paper discusses modelling methodology of the energy use of a certain amount of building stock at the city/regional/national level. In the methodology, building stock is divided into several stock categories and unit energy consumption for each category is quantified by performing simulations using prototypical building models as simulation input each representing a building stock category. For accurately modelling the energy use and estimate potential contributions of energy-conservation technologies, classification of the building stock is crucial as it homogenizes the stock group.

The objective of this work is to present an inverse method solving the transient heat-transfer problem in walls aiming to estimate its thermal properties. The procedure uses a finite difference numerical scheme, simulated in the environment SIMSPARK that is non object oriented and allows solving highly non linear problems. A method aiming to estimate building envelope thermal characteristics is elaborated knowing experimental in situ measurements.

Energy-efficiency seems to be one key-driver for whole building and construction industry in the future. Therefore, new construction and building service concepts are obviously needed. Most likely better thermal insulation levels and at least partly new heating and cooling solutions will be adopted. To avoid unpleasant indoor environment outcomes in future buildings, a holistic approach focusing on occupant aspects is recommended.

Knowing the presence or the actual number of occupants in a space at any given time is essential for the effec-tive management of various building operation functions such as security and environmental control (e.g., lighting, HVAC). In the past, motion detection using Passive In-frared (PIR) sensors has been widely deployed in com-mercial buildings and can provide data on “presence” sta-tus.

Contemporary office buildings commonly experience changes in occupancy patterns and needs due to changes in business practice and personal churns. Hence, it is important to understand and accurately capture the information of such trends for applications in building design and subsequent building operations. Detection of occupant presence has been used extensively in built environments for applications such as demand-controlled ventilation and security, and occupancy profiles are widely used in building simulations.

There has been extensive research focusing on developing smart environments by integrating data mining techniques into environments that are equipped with sensors and actuators. The ultimate goal is to reduce the energy consumption in buildings while maintaining a maximum comfort level for occupants. However, there are few studies successfully demonstrating energy savings from occupancy behavioural patterns that have been learned in a smart environment because of a lack of a formal connection to building energy management systems.

This article describes the development of a functionality generating simulations of commercial and institutional buildings with representative characteristics of a real estate stock in Québec. The functionality requires very little information such as the main building activity, the floor area and the construction year of the building.

This paper discusses the present status and implementation of a new energy model that is used for evaluating the impact of new technologies when they are applied to the Canadian housing stock (CHS). The model batch processes a database of nearly 17,000 real house descriptions that statistically represent the CHS. The model employs statistical and heat/mass transfer techniques to encompass energy consumption due to occupancy and thermal conditioning. So far, a majority of the features required for adequate building simulation have been implemented in the model.

Building energy and Carbon emission calculation methods for regions are of limited use if appropriate input data cannot be economically generated. To enable a wider uptake of regional modelling methods an automated analysis system is required to replace or assist time-consuming and expensive manual surveys of building stock. Building age is an important parameter in estimating energy use and Carbon emissions.