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REFIT: Personalised Retrofit Decision Support Tools for UK Homes using Smart Home Technology

REFIT: Personalised Retrofit Decision Support Tools for UK Homes using Smart Home Technology
REFIT:使用智能家居技术为英国家庭提供个性化改造决策支持工具
批准号:
EP/K002457/1
负责人:
Steven Firth
金额:
$94.38万
依托单位:
依托单位国家:
英国
项目类别:
Research Grant
财政年份:
2012
资助国家:
英国
项目状态:
已结题
起止时间:
2012 至 --

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中文摘要
翻译
热效率改造方案、电器升级和现场可再生能源是减少英国家庭能源需求的重要机会。通过隔热和气密性(特别是在20世纪80年代以前的住房中)减少热损失,升级家用电器库存(使用最新的节能型号)以及集成现场可再生能源和微型发电(发展“生产消费者”文化和减少能源账单)的潜力仍然在很大程度上没有实现。在向消费者提供改造解决方案的建议方面存在着一些挑战,这些建议将促进行为改变并影响购买决定。目前,消费者信息是基于标称房屋类型的标准化方法,由此产生的节能预测与现实的相似性最小,其中住宅的热效率,供暖系统和电器的效率,占用率,用户行为和偏好将对改造措施的有效性和采用产生重大影响。一种解决方案是为消费者提供个性化,准确和值得信赖的节能措施预测,这些预测是根据他们的居住和生活模式进行校准和定制的,以参与和促进行动的形式呈现。该提案将通过实施一种整体方法,为消费者提供个性化,量身定制的改造建议,并使用最大限度地提高消费者参与度的方法,促进英国家庭广泛采用改造措施。智能家居技术提供了一个独特的机会,可以使用实时测量,先进的数据分析,数字信号处理和通信技术,新颖的可视化,语义网和云计算技术,在不同的抽象层次上生成建议,以做出明智和合理的决策。智能家居概念目前正获得巨大的发展势头和开放系统的新发展,简单的使用和安装功能(即插即用),移动的访问(即智能手机)和连接已经引起了能源公司,ICT公司和家电制造商的注意。IBM对智能家居的愿景给出了三个特征:1)仪器化(传感器和家庭活动自动化); 2)互联(设备和更广泛的网络之间的通信-允许远程访问和控制设备); 3)智能(“基于数据做出决策的能力,导致更好的结果”)。智能家居为消费者提供了对家庭和能源系统的更多控制,更重要的是,通过干预措施可以减少能源需求和成本。该提案汇集了建筑,ICT,能源,设计和用户专家的多学科团队,为建筑围护结构改造,供暖系统和设备更换购买以及现场可再生能源集成开发个性化决策支持平台。这将为英国住户提供改造建议的准确性,从而实现低能耗和低碳的未来住房存量。这些成果将有利于:能源、信息通信技术、嵌入式系统和电信公司开发智能家居服务的技术和商业模式;消费者降低能源账单,提高家庭的安全性、保障性和舒适性;建筑部件、锅炉和家电制造商开发下一代低能耗产品;和政策制定者对创新方法的新见解,以满足英国能源系统的安全性,可负担性和碳减排的愿望。
英文摘要
Thermal efficiency retrofit options, appliance upgrades and on-site renewables represent a significant opportunity to deliver energy demand reductions to UK homes. The potential to reduce thermal heat losses through insulation and airtightness (in particular in pre-1980s housing), upgrade the household appliance stock (using the latest energy saving models) and integrated on-site renewables and microgeneration (developing a 'prosumer' culture and reducing energy bills) still remains largely unrealised. There are a number of challenges in providing advice for retrofit solutions to consumers which will promote behaviour change and influence purchasing decisions. Currently consumer information is based on standardised methodologies for nominal house types and the resulting predictions of energy savings have minimal resemblance to reality where the thermal efficiency of the dwelling, efficiency of heating system and appliances, occupancy, user behaviour and preferences will have a significant impact on the effectiveness and uptake of retrofit measures. One solution is to provide consumers with personalised, accurate and trustworthy predictions of energy saving measures which are calibrated and tailored to their dwelling and living patterns, presented in a format to engage and promote action. This proposal will facilitate a widespread uptake of retrofit measures in UK homes by implementing a holistic approach to providing consumers with personalised, tailored retrofit advice delivered using methods to maximise consumer engagement. Smart Home technology provides a unique opportunity to use real-time measurements, advanced data analytics, digital signal processing and communications techniques, novel visualisation, semantic web and cloud computing technologies to generate advice at different levels of abstraction for informed and justified decision making. The Smart Home concept is currently gaining significant momentum and new developments in open systems, simple use and installation features (ie plug and play), mobile access (ie Smart Phones) and connectivity have brought the concept to the attention of energy companies, ICT companies and appliance manufacturers. The IBM vision of a Smart(er) Home gives three characteristics: 1) Instrumented (sensors and automation of household activities); 2) Interconnected (communication between devices and wider networks - allowing remote access and control of devices); and 3) Intelligent ('the ability to make decisions based on data, leading to better outcomes'). Smart Homes provide consumers with more control over their homes and energy systems and, importantly, how their energy demand and costs can be reduced through interventions. This proposal brings together a multi-disciplinary team of building, ICT, energy, design and user experts to develop a personalised decision support platform for building envelope retrofits, heating system and appliance replacement purchases, and on-site renewables integration. This will deliver a step-change in the provision and accuracy of retrofit advice to UK householders leading to a low-energy and low-carbon future housing stock. The outcomes will be of benefit to: energy, ICT, embedded systems and telecommunication companies developing technology and business models for Smart Home services; consumers to lower their energy bills and improve the safety, security and comfort of their homes; building component, boiler and appliance manufacturers developing the next generation of low-energy products; and policy makers for new insights into innovative approaches to meeting the security, affordability and carbon reduction aspirations of the UK energy system.
期刊论文(10)
专著(0)
科研奖励(0)
会议论文
Data-driven simple thermal models: The radiator - gas consumption model
数据驱动的简单热模型:​​散热器 - 气体消耗模型
DOI: --
发表时间: 2015
期刊: 14th International Conference of the International Building Performance Simulation Association, Hyderabad, India, 07 Dec 2015 - 09 Dec 2015
影响因子: --
作者: [Dimitriou V]
通讯作者: Dimitriou V
DOI: 10.1016/j.apenergy.2021.116517
发表时间: 2021-03
期刊: Applied Energy
影响因子: 11.2
作者: [A. Ahmed;R. McLeod;Matej Gustin]
通讯作者: A. Ahmed;R. McLeod;Matej Gustin
Pre-installation challenges: classifying barriers to the introduction of smart home technology
安装前的挑战:对引入智能家居技术的障碍进行分类
DOI: --
发表时间: 2015
期刊: Joint Conference on 29th International Conference on Informatics for Environmental Protection / 3rd International Conference on ICT for Sustainability (EnviroInfo and ICT4S), Copenhagen, Denmark, 07 Sep 2015 - 09 Sep 2015
影响因子: --
作者: [De Oliveira L]
通讯作者: De Oliveira L
The role of programmable TRVs for space heating energy demand reduction in UK homes
可编程 TRV 在减少英国家庭空间供暖能源需求方面的作用
DOI: --
发表时间: 2014
期刊: 2nd IBPSA-England conference on Building Simulation and Optimization, London, 23 Jun 2014 - 24 Jun 2014
影响因子: --
作者: [Badiei A]
通讯作者: Badiei A
共 9 条
    Data for Digital Decarbonisation (3D): A FAIR approach to energy demand data in buildings
    • 批准号:
      EP/W027941/1
    • 项目类别:
      Research Grant
    • 资助金额:
      $5.94万
    • 财政年份:
      2021
    • 负责人:
      Steven Firth
    • 依托单位:
    海外基金