Building effectiveness communication ratios (BECs): an integrated ‘life-cycle’ methodology for mitigating energy-use in buildings

Current building regulations are generally prescriptive in nature. It is widely accepted in Europe that this form of building regulation is stifling technological innovation and leading to inadequate energy efficiency in the building stock. This has increased the motivation to move design practices towards a more ‘performance-based’ model in order to mitigate inflated levels of energy-use consumed by the building stock. A performance based model assesses the interaction of all building elements and the resulting impact on holistic building energy-use. However, this is a nebulous task due to building energy-use being affected by a myriad of heterogeneous agents. Accordingly, it is imperative that appropriate methods, tools and technologies are employed for energy prediction, measurement and evaluation throughout the project’s life cycle. This research also considers that it is imperative that the data is universally accessible by all stakeholders. The use of a centrally based product model for exchange of building information is explored. This research describes the development and implementation of a new building energy-use performance assessment methodology. Termed the Building Effectiveness Communications ratios (BECs) methodology, this performance-based framework is capable of translating complex definitions of sustainability for energy efficiency and depicting universally understandable views at all stage of the Building Life Cycle (BLC) to the project’s stakeholders. The enabling yardsticks of building energy-use performance, termed Ir and Pr, provide continuous design and operations feedback in order to aid the building’s decision makers. Utilised effectively, the methodology is capable of delivering quality assurance throughout the BLC by providing project teams with quantitative measurement of energy efficiency. Armed with these superior enabling tools for project stakeholder communication, it is envisaged that project teams will be better placed to augment a knowledge base and generate more efficient additions to the building stock.

Ironworking in late medieval Ireland, c. AD. 1200 to 1600

The landscape of late medieval Ireland, like most places in Europe, was characterized by intensified agricultural exploitation, the growth and founding of towns and cities and the construction of large stone edifices, such as castles and monasteries. None of these could have taken place without iron. Axes were needed for clearing woodland, ploughs for turning the soil, saws for wooden buildings and hammers and chisels for the stone ones, all of which could not realistically have been made from any other material. The many battles, waged with ever increasingly sophisticated weaponry, needed a steady supply of iron and steel. During the same period, the European iron industry itself underwent its most fundamental transformation since its inception; at the beginning of the period it was almost exclusively based on small furnaces producing solid blooms and by the turn of the seventeenth century it was largely based on liquid-iron production in blast-furnaces the size of a house. One of the great advantages of studying the archaeology of ironworking is that its main residue, slag, is often produced in copious amounts both during smelting and smithing, is virtually indestructible and has very little secondary use. This means that most sites where ironworking was carried out are readily recognizable as such by the occurrence of this slag. Moreover, visual examination can distinguish between various types of slag, which are often characteristic for the activity from which they derive. The ubiquity of ironworking in the period under study further means that we have large amounts of residues available for study, allowing us to distinguish patterns both inside assemblages and between sites. Disadvantages of the nature of the remains related to ironworking include the poor preservation of the installations used, especially the furnaces, which were often built out of clay and located above ground. Added to this are the many parameters contributing to the formation of the above-mentioned slag, making its composition difficult to connect to a certain technology or activity. Ironworking technology in late medieval Ireland has thus far not been studied in detail. Much of the archaeological literature on the subject is still tainted by the erroneous attribution of the main type of slag, bun-shaped cakes, to smelting activities. The large-scale infrastructure works of the first decade of the twenty-first century have led to an exponential increase in the amount of sites available for study. At the same time, much of the material related to metalworking recovered during these boom-years was subjected to specialist analysis. This has led to a near-complete overhaul of our knowledge of early ironworking in Ireland. Although many of these new insights are quickly seeping into the general literature, no concise overviews on the current understanding of the early Irish ironworking technology have been published to date. The above then presented a unique opportunity to apply these new insights to the extensive body of archaeological data we now possess. The resulting archaeological information was supplemented with, and compared to, that contained in the historical sources relating to Ireland for the same period. This added insights into aspects of the industry often difficult to grasp solely through the archaeological sources, such as the people involved and the trade in iron. Additionally, overviews on several other topics, such as a new distribution map of Irish iron ores and a first analysis of the information on iron smelting and smithing in late medieval western Europe, were compiled to allow this new knowledge on late medieval Irish ironworking to be put into a wider context. Contrary to current views, it appears that it is not smelting technology which differentiates Irish ironworking from the rest of Europe in the late medieval period, but its smithing technology and organisation. The Irish iron-smelting furnaces are generally of the slag-tapping variety, like their other European counterparts. Smithing, on the other hand, is carried out at ground-level until at least the sixteenth century in Ireland, whereas waist-level hearths become the norm further afield from the fourteenth century onwards. Ceramic tuyeres continue to be used as bellows protectors, whereas these are unknown elsewhere on the continent. Moreover, the lack of market centres at different times in late medieval Ireland, led to the appearance of isolated rural forges, a type of site unencountered in other European countries during that period. When these market centres are present, they appear to be the settings where bloom smithing is carried out. In summary, the research below not only offered us the opportunity to give late medieval ironworking the place it deserves in the broader knowledge of Ireland's past, but it also provided both a base for future research within the discipline, as well as a research model applicable to different time periods, geographical areas and, perhaps, different industries..

The application of computer modelling to develop a methodology for ‘existing building’ upgrades to move towards carbon-neutral buildings

The retrofitting of existing buildings for decreased energy usage, through increased energy efficiency and for minimum carbon dioxide emissions throughout their remaining lifetime is a major area of research. This research area requires development to provide building professionals with more efficient building retrofit solution determination tools. The overarching objective of this research is to develop a tool for this purpose through the implementation of a prescribed methodology. This has been achieved in three distinct steps. Firstly, the concept of using the degree-days modelling method as an adequate means of basing retrofit decision upon was analysed and the results illustrated that the concept had merit. Secondly, the concept of combining the degree-days modelling method and the Genetic Algorithms optimisation method is investigated as a method of determining optimal thermal energy retrofit solutions. Thirdly, the combination of the degree-days modelling method and the Genetic Algorithms optimisation method were packaged into a building retrofit decision-support tool and named BRaSS (Building Retrofit Support Software). The results demonstrate clearly that, fundamental building information, simplified occupancy profiles and weather data used in a static simulation modelling method is a sufficient and adequate means to base retrofitting decisions upon. The results also show that basing retrofit decisions upon energy analysis results are the best means to guide a retrofit project and also to achieve results which are optimum for a particular building. The results also indicate that the building retrofit decision-support tool, BRaSS, is an effective method to determine optimum thermal energy retrofit solutions.

Occupant location prediction in smart buildings using association rule mining

Heating, ventilation, air conditioning (HVAC) systems are significant consumers of energy, however building management systems do not typically operate them in accordance with occupant movements. Due to the delayed response of HVAC systems, prediction of occupant locations is necessary to maximize energy efficiency. We present an approach to occupant location prediction based on association rule mining, allowing prediction based on historical occupant locations. Association rule mining is a machine learning technique designed to find any correlations which exist in a given dataset. Occupant location datasets have a number of properties which differentiate them from the market basket datasets that association rule mining was originally designed for. This thesis adapts the approach to suit such datasets, focusing the rule mining process on patterns which are useful for location prediction. This approach, named OccApriori, allows for the prediction of occupants’ next locations as well as their locations further in the future, and can take into account any available data, for example the day of the week, the recent movements of the occupant, and timetable data. By integrating an existing extension of association rule mining into the approach, it is able to make predictions based on general classes of locations as well as specific locations.