English

A wide range of scientifically validated Building Performance Simulation tools BPS is available internationally. The users of those tools are mainly researchers, physicists and experts who value empirical validation, analytical verification and calibration of uncertainty as defined by e.g. BESTEST. However, literature and comparative surveys indicate that most architects who use BPS tools in design practice are much more concerned with the (1) Usability and Information Management (UIM) of interface and (2) the Integration of Intelligent design Knowledge-Base (IIKB).

The daylight factor is usually one of the first daylight performance measures that simulation newcomers calculate. Apart from the intrinsic limitations of the daylight factor as a meaningful daylighting performance metric, little work has been done in the past as to how accurate one can actually expect simulation novices to simulate the daylight factor compared to an expert modeler. This paper presents the comparison of daylight factor predictions from a ‘best practice’ model of an L-shaped perimeter classroom to a total of 69 novice/student models.

In the current context of energy crisis and with the debate on climate change, low-energy buildings are required.

A genetic optimization approach has been used for the design of an external shading device in an office with a window and different glass characteristics. The primary energy consumption for heating cooling and lighting have been minimized. Simulations have been performed using the energy code ESP-r and lighting simulation package Radiance, the optimization loop has been driven by the software tool modeFRONTIER. Different optimal geometries for a panel shading device have been found.

Recently, a combination between simulation and optimisation has been important for many of HVAC design problems.

Distributed energy system based on cogeneration system has high potential of energy saving due to utilizing waste heat from power generator effectively. However, unless the appropriate combination of machinery and operation are conducted, the expected performance is not achieved, it is quite difficult to determine the optimal combination of machinery and operation. Authors had already developed and proposed new optimal design method for building energy systems or distributed energy systems using Genetic Algorithm (GA) in some previous studies (e.g. Ooka R et al, 2008).

The aim of this paper is to describe the features of a Genetic Algorithm (GA) developed to solve simulation-based optimization problems for the optimal design of building parameters. This GA has been developed using guidelines from top researchs in the field of evolutionary computation. It is mostly based on NSGA-II and Omni-optimizer.

This paper presents a novel approach to derive U.S. residential building energy load profiles. This approach uses bootstrap sampling method to extract daily activity pattern of occupants of a household from American Time Use Survey (ATUS) data. The characteristics of ATUS data, the relation between time-use and load-demand, and the robustness of this approach are discussed. Virtual experiments were conducted on Energy Plus platform to study the patterns of annual load demand distribution under different household composition and thermal zoning schemes.

It is crucial to perform energy simulations during the building process in order to design a building that meets demands regarding low energy use. In a low energy building, internal heat gains such as excess heat from household electricity, are a large part of the heat balance of the building. The internal heat gains are depending on the occupants and not constant. Result from energy simulations with household electricity that varied during the day and the year according to a model based on measured data are presented.

This paper presents simulation results of three hypothetic models, which aims to represent different design decisions concerning administrative buildings at UFRN Campus, Brazil. Simulations were made with the DesignBuilder software. The analysis is intended to emphasize the influence of envelope architectural decisions on air conditioning energy consumption and the improvement of buildings thermal performance. The modelling process was supported by a field survey on 13 buildings at the Campus and by energy monitoring procedures.