The UK government aims to achieve a 60% reduction in UK carbon emissions by 2050. Energy efficiency is a key component of the UK Government's new climate change strategy with 50% of current carbon dioxide emissions resulting from energy use in buildings. Central to this approach is the ability to monitor and control energy consumption accurately. Building performance is often measured through the metered energy use of the whole building, and/or through the more detailed monitoring of the individually controlled processes by the building management systems (BMS). Automatic meter reading (AMR) systems have existed for several decades which can provide accurate consumption data, typically at half-hour intervals. However, with the recent introduction of a legal framework for supplier-independent metering, many initiatives are in place to install such meters in large numbers of industrial and domestic sites. This has resulted in a broad increase in uptake of advanced AMR systems and their associated services and the emergenceof the smart metering paradigm. Unfortunately, it is not clear how this data is actually going to be used. The advanced metering pilot of the Carbon Trust has produced a breakdown of recommendations derived from introducing advanced metering into SMEs: 15% of recommendations are the result of analysis of the data alone; 25% were the result of analysis of the data combined with advice by phone and email, and 60% required personal contact with energy experts. In other words, without additional advice, only a small fraction of the potential savings can actually be achieved.This project aims at those 85% of recommendations, investigating how Computational Intelligence (CI) techniques can help in generating the analysis needed to gain the full benefit from the data.Computational Intelligence techniques, like artificial evolution and neural network, are perfectly suited to the analysis of metering data. In general, the techniques require very little domain knowledge, can automatically acquire domain knowledge, and are tolerant to noise. Through machine learning, they can adapt to individual sites, and to changes over time. This research will investigate the potential for using Computational Intelligence methods in the automatic analysis of metered building energy data and how these methods can be used to provide maximum benefit to a large range of energy users. The project will concentrate on identifying CI techniques that can be broadly applied to wide ranges of sites, that can be largely automated, require minimal training, system setup and manual data entry.The project will establish potential techniques, benefits, and requirements, as well as the extent to which building specific knowledge is required in the analysis together with the impact of time-varying boundary conditions (the weather and occupant driven heatloads). While this proposal concentrates on energy consumption by buildings in commercial and industrial sites, the research will also provide valuable insights for energy consumption of domestic buildings, energy consumption in industrial processes, and any other metered utility consumption.