Purpose: – Within the building sector a lack of clarity in terminology does not help designers, clients or researchers. Non-domestic buildings have shown rapid increases in the use of advanced technology and control systems with varying drivers, many of which are labelled as intelligent. The term smart has been used interchangeably with intelligent without any clear distinction between the two. If the term Smart Buildings represented a separate, more advanced grouping, it would provide an opportunity to focus the future progress of non-domestic building development. The paper aims to discuss these issues. Design/methodology/approach: – Drawing upon academic and industrial literature and experience, this paper reviews the scope of Intelligent Buildings and the current available definitions of Smart Buildings to form a clear definition of both smart and Intelligent Buildings. Findings: – These definitions define the border between the intelligent and the (more advanced) Smart Building. The upper bound of the Smart Building is defined by (the future development of) the predictive building. Originality/value: – This work provides a clear focus which will allow the progression of the non-domestic building sector by providing guidance and aspiration, as well as providing a platform upon which a large amount of technical work can be based.

The change in climate has led to an interest in how this will affect the energy consumption in buildings. Most of the work in the literature relates to offices and homes. However, this paper investigates a supermarket in northern England by means of a multiple regression analysis based on gas and electricity data for 2012.The equations obtained in this analysis use the humidity ratio derived from the dry-bulb temperature and the relative humidity in conjunction with the actual dry-bulb temperature. These equations are used to estimate the consumption for the base year period (1961–1990) and for the predicted climate period 2030–2059.The findings indicate that electricity use will increase by 2.1% whereas gas consumption will drop by about 13% for the central future estimate. The research further suggests that the year 2012 is comparable in temperature to the future climate, but the relative humidity is lower. Further research should include adaptation/mitigation measures and an evaluation of their usefulness

Possible changes in gas and electricity consumption in supermarkets throughout Great Britain have been investigated for the 2030s in order to assist decision makers with choices relating to energy use. In addition to this, two operational procedures, which vary between supermarkets, were investigated to see if a link between them and differences in energy consumption could be established. To achieve these aims, seven similar supermarkets were identified and their data analysed to derive their energy signatures through simple and change point regression analysis. These models were then combined with data from climate change prediction project UKCP09 for different probabilities (10%, 50% and 90%) of temperature increase in order to calculate changes in future energy use. In addition it was investigated if a linear regression model between the selected operational procedures and electricity use could be established.The results showed that, compared with the base period 1961–1990, the mean values of the annual average temperature for these seven supermarkets was predicted to rise by 2.0°C or 20% for the central estimate. This led to an estimate of an increase in average electricity consumption of 2% and an average drop in the gas usage of 10%. The result also showed that differences in operational practices seem to have little impact on the in-store energy use. Differences in gas use models between stores can be more credibly explained by the building volume

Water loss from leaking pipes represents a substantial loss of revenue as well as environmental and public health concerns. Leak location is normally identified by placing sensors either side of the leak and recording and analysing the leak noise. The leak noise contains information about the leak's characteristics, including its shape. Whilst a tool which non-invasively provides information about a leak's shape from the leak noise would be useful for water industry practitioners, no tool currently exists. This study evaluates the effect of various leak shapes on the vibration signal and presents a unique methodology for predicting the leak shape from the vibration signal. An innovative signal processing technique which utilises the machine learning method random forest classifiers is used in combination with a number of signal features in order to develop a leak shape prediction algorithm. The results demonstrate a robust methodology for predicting leak shape at several leak flow rates within several backfill types, providing a useful tool for water companies to assess leak repair based on leak shape.