Advanced Analysis of Building Energy Performance using Computational Intelligence Approaches

英国政府的目标是到2050年实现英国碳排放减少60%。能源效率是英国政府新的气候变化战略的关键组成部分，目前50%的二氧化碳排放来自建筑物的能源使用。这种方法的核心是准确监测和控制能源消耗的能力。建筑物的性能通常通过整个建筑物的计量能源使用量来衡量，和/或通过建筑物管理系统(BMS)对单独控制的过程进行更详细的监测来衡量。自动抄表(AMR)系统已经存在了几十年，它可以提供准确的消耗数据，通常是以半小时为间隔。然而，随着最近引入了独立于供应商的计量的法律框架，许多倡议已经到位，在大量的工业和家庭现场安装这种仪表。这导致先进的自动抄表系统及其相关服务的广泛采用，以及智能计量范例的出现。不幸的是，目前还不清楚这些数据实际上将如何使用。Carbon Trust的高级计量试点对将先进计量引入中小企业所产生的建议进行了细分：15%的建议是仅分析数据的结果；25%的建议是结合电话和电子邮件的建议对数据进行分析的结果，60%的建议需要与能源专家进行个人接触。换句话说，没有额外的建议，实际上只能实现一小部分潜在的节省。该项目针对这85%的建议，调查计算智能(CI)技术如何帮助生成从数据中获得全部好处所需的分析。计算智能技术，如人工进化和神经网络，非常适合于计量数据的分析。一般而言，这些技术需要很少的领域知识，可以自动获取领域知识，并且对噪声具有容忍性。通过机器学习，他们可以适应各个站点，并适应随着时间的推移而发生的变化。这项研究将调查计算智能方法在计量建筑能源数据自动分析中的潜力，以及如何使用这些方法为广泛的能源用户提供最大利益。该项目将专注于确定可广泛应用于各种地点的CI技术，这些技术可在很大程度上实现自动化，只需最少的培训、系统设置和手动数据输入。该项目将确定潜在的技术、好处和要求，以及在分析中需要建立特定知识的程度，以及随时间变化的边界条件(天气和人员驱动的热负荷)的影响。虽然这项建议集中于商业和工业场所建筑物的能源消耗，但研究也将为家庭建筑物的能源消耗、工业过程中的能源消耗以及任何其他计量的公用事业消耗提供有价值的见解。

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.