Building energy

The work reported in this paper extends previous work on the feasibility to characterise air leakage and mechanical ventilation avoiding intrusiveness of traditional measurement techniques. The feasibility to obtain the air renovation rate itself, as well as the possibilities to express it as function of other variables (such as wind speed, atmospheric pressure, etc.), are studied. Tracer gas measurements based on N2O have been used as reference.

Modelling and simulation of energy consumption in Information Technology (IT) building on the Rockhampton campus of Central Queensland University, Australia is presented. Design Builder, commercially available software, was used for the prediction of energy consumption. All the possible sources and uses of energy in building were accounted in the modelling and simulation. The operation of the Heating, Ventilation and Air Conditioning (HVAC) system and the lighting energy consumption of the whole building has been studied in detailed.

Given the major role played by windows with regard to energy losses and gains from buildings in respectively cold and hot climates, accurate prediction of the heat transfer through its glazing materials is of great importance in building energy simulation. In most of the building energy simulation programs, solar radiation absorption inside glazing layers is usually treated considering that all the radiation is uniformly absorbed in the glazing.

The nighttime ventilation strategy uses the outdoor cold air during the night to cool the building mass. The cooled building mass then is used as a heat sink during the next hot day. Mechanical nighttime ventilation requires a fan for the outside air ventilation. The energy use by the fan reduces the potential cooling energy savings. Higher nighttime ventilation flow rate and its duration decrease required cooling energy during next hot day in the building, also they increase fan energy consumption.

This paper discusses how the consumption pattern of occupants and architectural design of buildingsaffect the building energy consumption. An overview of the building energy consumptions in Hong Kongis presented. Electricity consumption data in Hong Kong may have resulted in various indications andestimates of the inevitable growth in electricity demand. The growth rate varies in different sectors anddepends on the end-use pattern together with the economic activity. The increasing standard of livingcauses greater need of resources and sometimes may lead to abusive consumptions.

Outdoor testing of building and building components under real weather conditions provide useful information about the dynamical performance. Such knowledge is needed for properly characterizing the heat dynamics and provides useful information that can be used eg. to implement energy saving strategies. In order to analyse such tests models and methods for dynamic analysis are required. However a wide variety of models and methods exists, and the problem of choosing the most appropriate approach for every particular case is a non-trivial and interdisciplinary problem.

This paper presents the application of IDENT Graphical User Interface of MATLAB to estimate the thermal properties of building components from outdoors dynamic testing, imposing appropriate physical constraints and assuming linear and time invariant parametric models. Theory is briefly described to provide the background for a first understanding of the used models. The relationship between commonly used RC-network models and the parametric models proposed is deduced. The analysis is generalised for different possibilities in the assignment of inputs and outputs and even multioutput.

Dynamic analysis is very useful to obtain thermal properties of building components. However different methodologies can be applied, and several software tools based on these methodologies are available which application requires certain degree of experience. The main differences between methodologies are the numerical procedure and the model assumed to estimate the required parameters. Different approaches can lead to different and even wrong estimations of the parameters.