Energy literacy through an intelligent home energy advisor (ENLITEN)

英国承诺将人类造成的温室气体排放量减少80%。除财政奖励措施外，减少碳排放还需要提高“能源素养”，即要求公众更好地了解其行为和习惯对能源、碳和财政的影响。Enliten项目旨在通过了解和影响居住者在能源使用方面的习惯和行为来减少建筑物内能源使用产生的碳排放。通过内部物理控制(如自动关闭窗户)显著减少建筑物内的能源消耗在经济上是困难的。例如，为窗户配备传感器和马达，每扇窗户的成本约为100 GB。通过外部政策控制来减少建筑物内的能源消耗，例如强制执行家电可以运行和不可以运行的时间，在社会和政治上都是困难的。例如，当加州试图在全州范围内将空调温度设置降低1华氏度时，引发了公众的愤怒和抵制。因此，一个在经济、社会和政治上更有可能实现显著节能的方法是说服建筑物居住者改变他们的能源消费行为。已经有许多关于向建筑物居住者提供他们的能源使用信息对能源需求的影响的研究，其基础是希望这些信息将鼓励他们减少使用。结果差异很大，表明降幅从0%到20%不等。如果减少是通过居住者行为的改变来实现的，那么从长远来看，这种减少往往不会持续。为了实现建筑物居住者能源消耗的显著持续减少，我们需要避免简单地提供更多信息--这种方法在其他领域已经失败--而是专注于提供具有超越任何临时干预或活动的影响的信息。这可以通过鼓励改变长期持续的可持续行为来实现，在最大限度地减少所需行为改变的负担的同时，最大化每个人的节省，以及最大限度地增加做出改变的个人数量。为了实现这些目标，我们将特别着眼于长期持续的影响，重点是改变建筑物居住者的习惯行为，而不仅仅是对干预措施的短期反应。我们将开发一种创新的智能系统，为每个家庭提供个性化的信息、建议和奖励，并与新颖的行为驱动型能源关税相关联。我们将通过使设备便宜、易于部署和对建筑结构的破坏降至最低，来最大限度地提高系统的可及性和潜力。该系统将基于一个完整的建筑能源模型，该模型独特地集成了建筑物的热力模型、居住者的习惯和要求模型以及建筑物内的能源使用的分类模型。我们将使用最小传感器集的数据来开发一个独特的自动生成的建筑热力模型，以及一个能源使用的分解模型。我们将使用一系列自动化和人工数据收集和分析来建立对居住者与能源相关的态度、行为和习惯的理解和模型。我们将把这些模型结合在一起，形成一个互动的建筑内工具，帮助居住者识别和打破不良的能源习惯，形成更好的习惯，并减少能源需求和碳排放。在促进居住者习惯变化的同时，我们将把这些变化与更广泛的社会和经济背景联系起来，研究行为变化的价值和成本之间的权衡，以个人和能源供应链的能源成本和碳足迹的减少来量化。这一分析将使我们能够开发新的基于关税的激励措施，奖励预期的行为变化。

The UK is committed to an 80% reduction in human-created greenhouse gas emissions. As well as financial incentives, carbon reduction will require an increase in "energy literacy", i.e. it will require members of the public to better understand the energy, carbon and financial implications of their behaviours and habits. The ENLITEN project aims to reduce carbon emissions from energy use within buildings by understanding and influencing occupants' habits and behaviours around energy use.Significantly reducing energy use within buildings through internal physical controls, such as automatically closing windows, is difficult economically. For example, equipping windows with sensors and motors would cost in the region of £100 per window. Reducing energy use within buildings through external policy controls, such as enforcing times when appliances can and cannot be run, is difficult socially and politically. For example, when California tried to impose a state-wide reduction of 1F in air-conditioning temperature settings, there was public outrage and resistance. Hence, an approach that has more chance - economically, socially and politically - of achieving significant energy reductions is to persuade building occupants to change their energy consuming behaviours.There have been many studies of the effect on energy demand of providing building occupants with information on their energy use, founded on the hope that such information will encourage them to reduce their use. The results vary widely, suggesting anything from 0% to 20% reductions. Where reductions are achieved through occupants' behavioural changes, they are often not sustained in the longer term. To achieve significant sustained reductions in energy use by building occupants, we need to avoid simply presenting more information - an approach that has failed in other domains - and focus on providing information that has an effect which lasts beyond any temporary interventions or campaigns.This may be achieved by encouraging changes to sustainable behaviours that are sustained in the longer term, maximising the savings by each individual while minimising the burden of behavioural change required, and maximising the number of individuals making changes. In order to achieve these goals, we will specifically target long term sustained effects by focusing on changes to the habitual behaviours of building occupants and not just short-term responses to interventions. We will develop an innovative smart system that provides information, recommendations and rewards personalised to each household and associated with novel behaviour-driven energy tariffs. We will maximise accessibility and potential uptake of the system by making the equipment cheap, easily deployable and minimally disruptive to the building fabric.The system will be based on a whole building energy model that, uniquely, integrates a thermal model of the building, a model of occupants' habits and requirements and a disaggregated model of energy use in the building. We will use data from a minimal sensor set to develop a unique auto-generated thermal model of the building, and a disaggregated model of energy use. We will use a range of automated and human data collection and analyses to develop an understanding and model of occupants' energy- related attitudes, behaviours and habits. We will bring these models together to inform an interactive in-building tool to help occupants identify and break poor energy habits, form better ones and reduce energy demand and carbon emissions. While fostering changes in the habits of the occupants, we will relate these changes to the broader social and economic context, examining the trade-offs between the value and costs of behavioural change, quantified in terms of reductions in energy cost and carbon footprint for individuals and the energy supply chain. This analysis will allow us to develop novel tariff-based incentives that reward desired behavioural changes.