New Empirically-Based Models of Energy Use in the Building Stock

减少二氧化碳和保障能源供应的国家计划有赖于建筑部门非常显著和迅速的减排。实现这一转型将需要大量有效的技术和政策干预。这些反过来将取决于更好地了解目前建筑存量中的能源使用模式，并将这种了解纳入新的预测模型。该项目将致力于为住宅和非住宅建筑(即除房屋和公寓以外的所有建筑)的国家库存发展这方面的知识。能源和气候变化部(DECC)正在建立一个国家能源效率数据库(NEED)，在该数据库中，关于住宅和非住宅的信息将在个人物业的水平上与其实际的天然气和电力消耗联系起来。本项目的目的是配合和支持需求的发展。国内库存数据库--家庭能源效率数据库(HEED)的工作进展顺利，该数据库目前包含约1300万套住房、其类型和结构、能源使用以及安装了哪些节能措施的信息。实际上，Heed将在适当的时候与需求联系在一起。到目前为止，关于需求的非国内部分的工作还没有取得进展。考虑到需求的进一步发展，本项目提出了几个方面的工作。将编制和加强申请者在个人房舍一级建立的现有非住宅库存数据库和模型，纳入各种来源的新数据。与此同时，将建立一个单独的新模型，结合汇总数据，跟踪近年来能源消耗的趋势，并试图确定气候、经济活动、建筑面积增长、燃料价格变化和能效提高的各种影响。这些模型仅根据建筑面积和单位建筑面积的能源使用率运行(需要的非住宅部分也是如此)。它们不把建筑物作为单元来处理，尽管建筑物的几何形状和结构对能源使用很重要。该项目将探索将非住宅建筑面积与建筑及其建筑联系起来的新方法，使用来自数字地图、城市3D数字模型和照片数据库(如Google StreetView)的信息。在EPSRC资助的前一个项目中，该团队已经对HEED数据库进行了广泛的分析，以研究当前家庭能源使用的模式。本项目的计划是在这项工作的基础上再接再厉，并研究一些新问题。理论模型通过不同测量方法预测的节能水平与从经验测量(如HEED)观察到的实际节能水平之间可能存在显著差异。可能有几种原因，包括所谓的“反弹”或“收回”效应，即居住者对能源改善的反应，比如享受更高的温度，更多的房间供暖，或者更频繁地使用电器。相反，如果住户对能源在家庭中的确切使用方式和地点有更好的信息，他们就有可能减少能源的消耗。通过对更频繁的计量数据进行分析，可以在一定程度上观察到这种行为影响，这些数据来自所谓的“智能电表”。为了更好地理解这些反馈现象，该项目建议将智能电表的数据与普通的“哑巴”电表的数据进行比较。然后，可以在改进的预测模型中考虑到这些因素，这些模型可以用来支持政府在未来几十年翻新住房存量的计划。

National plans for CO2 reduction and security of energy supply depend on very significant and rapid reductions in the building sector. Delivering this transformation will require a raft of effective technology and policy interventions. These in turn will depend on much better knowledge of the present patterns of energy use in the building stock, and the incorporation of this understanding into new predictive models. The project will seek to contribute to developing this knowledge for the national stocks of both domestic and non-domestic buildings (i.e. all buildings other than houses and flats). Greater emphasis will be placed on non-domestic buildings, since here the state of current knowledge is weaker.The Department of Energy and Climate Change (DECC) is in the process of constructing a National Energy Efficiency Database (NEED) in which information about dwellings and non-domestic premises is being linked to their actual gas and electricity consumption, at the level of individual properties. The present project is intended to run alongside and support the development of NEED. Work is well advanced on a domestic stock database, the Household Energy Efficiency Database (HEED), which currently contains information on some 13 million dwellings, their types and construction, their use of energy, and what energy-saving measures have been installed. HEED will in due course, in effect, be linked into NEED. Work on the non-domestic part of NEED is not so far advanced. In anticipation of the further development of NEED, this project proposes several strands of work. An existing database and model of the non-domestic stock at the level of individual premises, developed by the applicants, will be elaborated and strengthened with the incorporation of new data from a variety of sources. Meanwhile a separate new model will be built, working with aggregated data, to follow trends in energy consumption over recent years and to try to determine the various effects of climate, economic activity, growth in floor area, changes in fuel price, and efficiency improvements.These models operate just with floor areas and rates of energy use per unit of floor area (as will the non-domestic part of NEED). They do not deal with buildings as units, even though the geometry and construction of buildings are important for energy use. The project will explore new methods for relating non-domestic floor areas to buildings and their construction, using information from digital maps, 3D digital models of cities, and photographic databases such as Google StreetView.In a previous EPSRC-funded project the team has already carried out extensive analyses of the HEED database to study current patterns of domestic energy use. The plan in the present project is to build on that work, and to study some new issues. There can be significant differences between the levels of energy savings predicted from different measures by theoretical models, and actual savings as observed from empirical measurements (as in HEED). There are likely to be several causes, including so-called 'rebound' or 'take-back' effects, where the occupants react to energy improvements by for example enjoying higher temperatures, heating more rooms, or using appliances more frequently. Conversely it is possible that householders may reduce their consumption of energy if they have better information about exactly how and where that energy is being used in the home. Such behavioural effects can be observed to an extent through analysis of much more frequent metering data, derived from so-called 'smart meters'. The project proposes to compare data for the same dwellings from smart meters with data from normal 'dumb' meters (as in HEED), in order to try to better understand these feedback phenomena. These can then be allowed for in improved predictive models, which can be used to support the government's programme of refurbishment of the housing stock over the coming decades.