Office building characteristics and the links with carbon emissions

Purpose – The purpose of this paper is to present research which analysed energy consumption in the Melbourne central business district (CBD) office stock and examined all buildings to identify CO2 emissions in 2005. The rationale was that, by profiling a large group of buildings, it would be possible to identify characteristics of the stock. For example, do older buildings typically emit more CO2 per square metre than newer buildings?
Design/methodology/approach – This research conducted a detailed analysis of all Melbourne CBD office stock to identify which patterns and trends emerged regarding building characteristics and carbon emissions. The study examined variables such as building size, number of employees, occupancy levels, physical characteristics and building age.
Findings – By examining all office stock and aggregating data, the results confirm that it is possible to identify general physical building characteristics and carbon emissions. This research confirmed that clear relationships existed within the Melbourne CBD office stock in terms of building size, age and the density of occupation in relation to CO2 emissions.
Originality/value – Practitioners can apply this knowledge to the professional advice they give to clients to assist in achieving increased energy efficiency in the office stock, for example in refurbishment being conscious that smaller buildings will be generally less energy-efficient than larger ones.

Cooperative relaying in wireless sensor networks

Cooperative relaying has been shown to be an effective method to significantly improve the error-rate performance in wireless networks. This technique combats fading by exploiting the spatial diversity made available through cooperating nodes that relay signals for each other. In the context of wireless sensor networks, cooperative relaying can be applied to reduce the energy consumption in sensor nodes and thus extend the network lifetime. Realizing this benefit, however, requires a careful incorporation of this technique into the routing process to exploit diversity gains. In this chapter, we introduce the basic concepts required to understand cooperative relaying and review current state of the art energy-efficient routing protocols that realize cooperative relaying.

Investigation of yarn hairiness

Yarn hairiness affects not only the quality of products, but also the productivity in spinning and weaving. Too much yarn hairiness is undesirable for many end uses as well as the spinning and post spinning processes. The main aims of this project are to examine the hairiness features of various yarns and to reduce yarn hairiness. The thesis covers five related areas – hairiness assessment, factors affecting yarn hairiness, the hairiness of newly developed yarns, yarn hairiness reduction, and effect of yarn hairiness on the energy consumption in ring spinning. The worsted cashmere, pure wool and wool/cashmere blend yarns were employed to investigate the effect of some fibre parameters on the yarn hairiness. A single exponential distribution of the hair-length was confirmed first, using the data from the Zweigle G565 Hairiness Meter. A linear relationship was observed between the blend ratio and the hairiness indexes. In particular, the effect of fibre crimp or curvature on yarn hairiness is examined. The theory of yarn hairiness composition was also developed further. The effect of draft ratio and spindle speed on the hairiness of worsted wool yarn was examined next with a factorial experiment design. Several new hairiness indexes, namely the relative hairiness indexes, have been used to explain the results obtained. In the investigation of the hairiness of newly developed yarns, the hairiness of the Compact Spun and Roller-Jet-Spun yarns was examined first. The composition of the yarn hairiness, the hair-length distribution, and the effect of test speed on yarn hairiness were then studied. An important finding is that for both yarns, the predominant hairiness feature is the looped hairs. A comparison of the hairiness of Solospun yarns and the equivalent ring spun wool yarns was undertaken. The hair-length distribution of the Solospun yarn was examined first. The Solospun yarns used had fewer hairs in most hair-length groups and lower variations in hairiness. In addition, the effect of twist level and spindle speed on the hairiness of Solospun and conventional ring spun yarns has also been discussed. A novel approach of reducing yarn hairiness – spinning with a ‘Diagonal’ yarn path was examined next. Both ‘Left Diagonal’ and ‘Right Diagonal’ yarn arrangements were studied. A new finding is that the ‘Right Diagonal’ yarn path leads to reduced hairiness for the Z-twist yarn, while yarn evenness and tenacity are not as sensitive to the modified yarn path. The mechanism of hairiness reduction with the ‘Diagonal’ yarn path has been discussed. The spinning performance of “Right Diagonal” yarn arrangement has also been evaluated. Finally, the effect of yarn hairiness on the energy consumption in ring spinning has been investigated theoretically and experimentally. A theoretical model has been developed, which represents the first attempt at theoretically investigating the influence of yarn hairiness on energy consumption during the winding stage of ring spinning. The experimental results have generally confirmed predictions of this model. Recommendations for further research in this area have also been made in the concluding chapter of this thesis.

Using input-output data in life cycle inventory analysis

The impacts on the environment from human activities are of increasing concern. The need to consider the reduction in energy consumption is of particular interest, especially in the construction and operation of buildings, which accounts for between 30 and 40% of Australia's national energy consumption. Much past and more recent emphasis has been placed on methods for reducing the energy consumed in the operation of buildings. With the energy embodied in these buildings having been shown to account for an equally large proportion of a building's life cycle energy consumption, there is a need to look at ways of reducing the embodied energy of buildings and related products. Life cycle assessment (LCA) is considered to be the most appropriate tool for assessing the life cycle energy consumption of buildings and their products. The life cycle inventory analysis (LCIA) step of a LCA, where an inventory of material and energy inputs is gathered, may currently suffer from several limitations, mainly concerned with the use of incomplete and unreliable data sources and LCIA methods. These traditional methods of LCIA include process-based and input-output-based LCIA. Process-based LCIA uses process specific data, whilst input-output-based LCIA uses data produced from an analysis of the flow of goods and services between sectors of the Australian economy, also known as input-output data. With the incompleteness and unreliability of these two respective methods in mind, hybrid LCIA methods have been developed to minimise the errors associated with traditional LCIA methods, combining both process and input-output data. Hybrid LCIA methods based on process data have shown to be incomplete. Hybrid LCIA methods based on input-output data involve substituting available process data into the input-output model minimising the errors associated with process-based hybrid LCIA methods. However, until now, this LCIA method had not been tested for its level of completeness and reliability. The aim of this study was to assess the reliability and completeness of hybrid life cycle inventory analysis, as applied to the Australian construction industry. A range of case studies were selected in order to apply the input-output-based hybrid LCIA method and evaluate the subsequent results as obtained from each case study. These case studies included buildings: two commercial office buildings, two residential buildings, a recreational building; and building related products: a solar hot water system, a building integrated photovoltaic system and a washing machine. The range of building types and products selected assisted in testing the input-output-based hybrid LCIA method for its applicability across a wide range of product types. The input-output-based hybrid LCIA method was applied to each of the selected case studies in order to obtain their respective embodied energy results. These results were then evaluated with the use of a number of evaluation methods. These evaluation methods included an analysis of the difference between the process-based and input-output-based hybrid LCIA results as an evaluation of the completeness of the process-based LCIA method. The second method of evaluation used was a comparison between equivalent process and input-output values used in the input-output-based hybrid LCIA method as a measure of reliability. It was found that the results from a typical process-based LCIA and process-based hybrid LCIA have a large gap when compared to input-output-based hybrid LCIA results (up to 80%). This gap has shown that the currently available quantity of process data in Australia is insufficient. The comparison between equivalent process-based and input-output-based LCIA values showed that the input-output data does not provide a reliable representation of the equivalent process values, for material energy intensities, material inputs and whole products. Therefore, the use of input-output data to account for inadequate or missing process data is not reliable. However, as there is currently no other method for filling the gaps in traditional process-based LCIA, and as input-output data is considered to be more complete than process data, and the errors may be somewhat lower, using input-output data to fill the gaps in traditional process-based LCIA appears to be better than not using any data at all. The input-output-based hybrid LCIA method evaluated in this study has shown to be the most sophisticated and complete currently available LCIA method for assessing the environmental impacts associated with buildings and building related products. This finding is significant as the construction and operation of buildings accounts for a large proportion of national energy consumption. The use of the input-output-based hybrid LCIA method for products other than those related to the Australian construction industry may be appropriate, especially if the material inputs of the product being assessed are similar to those typically used in the construction industry. The input-output-based hybrid LCIA method has been used to correct some of the errors and limitations associated with previous LCIA methods, without the introduction of any new errors. Improvements in current input-output models are also needed, particularly to account for the inclusion of capital equipment inputs (i.e. the energy required to manufacture the machinery and other equipment used in the production of building materials, products etc.). Although further improvements in the quantity of currently available process data are also needed, this study has shown that with the current available embodied energy data for LCIA, the input-output-based hybrid LCIA appears to provide the most reliable and complete method for use in assessing the environmental impacts of the Australian construction industry.

Developing an ‘as performing’ building assessment

The building profession is increasingly becoming more demanding with respect to building environmental performance. Intentions are to provide best practices into our buildings. In part, this is a response due to the Australian government and other independent organisations that have developed policy on rating tools and performance ranking measures, all with the intention of accomplishing environmentally sustainable buildings.

With rating systems endorsing innovative environmental design solutions, it could be asked: Are our buildings really operating as rated? Do we know whether our designs are in compliance with what was calculated or simulated? Is there a feedback loop informing the design process on successes or failures in our designs or mechanical services?

While ratings continue to focus on ‘by design’ or ‘as built’ rewards, few tools acknowledge perhaps the more crucial bottom line: ‘as performing’. With the exception of an AGBR (Australian Green Building Rating) scheme on actual annual energy consumption, there appears to be no ‘as performing’ assessment. Furthermore, practically every building is a prototype (a one-off) and requires commissioning, programming and scheduling of its services. It would certainly appear that as stakeholders (the procurers, owners, facilities managers and users) of the newly built environment, that what we really want to know is actual on-site confirmation of performance. It is the objective of the Mobile Architecture and Built Environment Laboratory (MABEL), to provide such a service.

Power saving in sensor networks via mobile base station repositioning

This research investigated the effect of dynamically repositioning the geographic location of a mobile base station within a sensor network in order to reduce energy consumption and increase network lifetime. Through simulation and experimental methodology, the proposed approach outperformed existing methods by extending network lifetime whilst reducing energy consumption.

Dynamic request-driven scheme for data collectors in sensor networks

Wireless sensor networks lifetime is prolonged through a dynamic scheme for collecting sensory information using intelligent mobile elements. The data collection routes are optimised for fast and reliable delivery. The scheme minimises high levels of energy consumption to extend the network operational time.

Intelligent based mobility models for data management in wireless sensor networks

Wireless sensor networks (WSNs) are proposed as powerful means for fine grained monitoring in different classes of applications at very low cost and for extended periods of time. Among various solutions, supporting WSNs with intelligent mobile platforms for handling the data management, proved its benefits towards extending the network lifetime and enhancing its performance. The mobility model applied highly affects the data latency in the network as well as the sensors’ energy consumption levels. Intelligent-based models taking into consideration the network runtime conditions are adopted to overcome such problems. In this chapter, existing proposals that use intelligent mobility for managing the data in WSNs are surveyed. Different classifications are presented through the chapter to give a complete view on the solutions lying in this domain. Furthermore, these models are compared considering various metrics and design goals.