English

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.

In this study we are investigating the urban environment, including complicated social dynamics, as the “urban system”. We have developed a simulator which can predict the environmental load for a city, comprised mainly of buildings, over the medium and long term. In this paper we describe the development of this simulator and verify its accuracy by comparing the calculated values with real data, investigating the population, housing, non-residential building, traffic and environment sectors, all elements of the simulator 

This paper proposes a set of principles for an international database of building materials that would meet Quality Criteria for use in building performance simulation. The proposal draws inspiration from the International Glazing Data Base, but suggests that this inspiration goes as far as the quality assurance goal, not the practice.

Building designers need design tools that enable them to rapidly explore the energy performance implication of early design decisions.  The tools should enable them to use their experience, along with performance feedback, to find near-optimal solutions, according to their criteria.  This paper presents a methodology for a solar house design, followed by a description of how it will be implemented in a design tool.

With the current focus on energy performance certification, factors such as increased and unstable fuel prices and consequent heating and electrical costs as well as the large number of buildings that are going to require energy analysis, a real need has surfaced for powerful, fast, and easy to use energy analysis solutions that will not be limited to energy experts.

Parametric analysis is a powerful method for exploring alternative design options and establishing variable dependency therefore design guide. The text-based user interface of EnergyPlus makes it a perfect simulation tool for automated (or scripted) parametric analysis. Since the number of simulations required for parametric analysis tend to be large, a software utility that may take advantage of the ever-increasing desktop computing power is desirable. “Parallelism”, in its broad sense of running more calculations simultaneously, comes naturally into our view.

We address the model-adaptive coupling between computational codes for indoor thermal comfort analysis considering different levels of detail in space and time. Starting with a whole-year simulation, sig-nificant periods are interactively identified in terms of a coarse thermal comfort analysis. After refining these critical intervals with respect to the spatial reso-lution, a multi-segment manikin model interfacing with the human thermoregulation model of Fiala (Int J Biometeorol, 45:143–159, 2001) is applied for studying transient and local effects of thermal sensa-tion.