Reducing End Use Energy Demand in Commercial Settings Through Digital Innovation

通过数字创新减少商业环境中的最终使用能源需求

• 批准号: EP/T025964/1
• 负责人: Adrian Friday
• 金额: $ 207.14万
• 依托单位: Lancaster University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2021
• 资助国家: 英国
• 起止时间: 2021 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FT025964%2F1
• 关键词: Reducing; End; Use; Energy; Demand

The UK, Ireland, Canada and France have all declared climate emergencies. Climate change has never had a more prominent in the public eye. With legal commitments to reduce greenhouse gas emissions by at least 80% by 2050 relative to 1990 levels, it has never been more important to do everything we can to reduce energy demand. The promise in this project is to help provide new methods for analysing the 'data deluge' of energy and building system data (from IoT devices) that can help unlock energy efficiencies and identify the benefits of energy efficiency measures despite noisy and heterogeneous data; and make it cheap, repeatable and routine to do this on an ongoing basis. Key to our approach are novel statistical and mixed-method techniques working closely with our project partners and their data to demonstrate the feasibility of these benefits. Our ultimate goal is to make it possible to translate the savings found in one context to another (e.g. another similar building, or even similar business). This would enable the 'digital replication' of energy efficiency savings, and even an almost viral spread of the knowledge and technique across sectors---with massive potential.Currently for many organisations, making sense of this rich source of information defies the human resource available to analyse and profit from the potential insights available. Such analysis is currently the domain of specialist consultancy providers due to the significant cost, time and know-how required to identify opportunities in the data. This restricts the penetration of data-driven monitoring and energy reduction strategies, and the opportunities for knowledge transfer across different locations and businesses. This project will clear this analysis bottleneck.The approach builds on foundations in modern data science, applying cutting edge techniques to automatically identify problems at particular sites and recommend interventions based on cross-site comparisons. The principle objective is to enable commercial sites to reduce their energy demand and keep it low without requiring energy analysts to manually investigate each site individually, at further expense.Core to our approach are next-generation statistics and machine learning methods applied to a unique corpus of fine-grained energy and process data sourced from our partners (BT, Tesco, Lancaster University Facilities (a town sized campus), and energy management consultancy and cloud energy analytics provider, BEST). This will enable us to apply cutting edge statistical techniques to a very significant data set in this domain for the first time.More specifically, our main aims are to:1. develop automated techniques for supporting analysis, identifying and recommending energy savings strategies, based on the application of statistical and machine learning techniques to fine-grained energy data;2. derive knowledge of how, where and when energy is used, to identify opportunities to reduce and shift demand by comparing differences in energy use over time within and between premises;3. support regular and repeated analysis, towards a continual improvement in energy reduction over time.4. provide open source, permissively licensed implementations for enabling uptake, even beyound our project partners and their partner networks. Our publication and publicity strategies will maximise exposure of our project results to various stakeholder groups including academia, practitioners, and key industry stakeholders.

英国、爱尔兰、加拿大和法国都宣布进入气候紧急状态。气候变化从来没有在公众眼中如此突出。随着法律的承诺，到2050年将温室气体排放量相对于1990年的水平减少至少80%，尽我们所能减少能源需求从未如此重要。该项目的承诺是帮助提供分析能源和建筑系统数据（来自物联网设备）的“数据洪水”的新方法，这些方法可以帮助释放能源效率，并确定能源效率措施的好处，尽管有噪音和异构数据;并使其廉价，可重复和常规化。我们的方法的关键是与我们的项目合作伙伴及其数据密切合作的新型统计和混合方法技术，以证明这些好处的可行性。我们的最终目标是将在一个环境中发现的节省转化为另一个环境（例如另一个类似的建筑，甚至类似的业务）。这将使能源效率节约的“数字复制”成为可能，甚至使知识和技术在各部门之间几乎像病毒一样传播-具有巨大的潜力。目前，对于许多组织来说，要理解这种丰富的信息来源，需要人力资源来分析并从潜在的见解中获利。由于需要大量的成本、时间和专门知识来确定数据中的机会，这种分析目前属于专业咨询提供商的领域。这限制了数据驱动的监测和节能战略的渗透，以及在不同地点和业务之间进行知识转移的机会。该项目将消除这一分析瓶颈。该方法建立在现代数据科学的基础上，应用尖端技术自动识别特定站点的问题，并根据跨站点比较推荐干预措施。我们的主要目标是使商业站点能够减少其能源需求并保持在较低水平，而无需能源分析师单独手动调查每个站点，从而增加成本。我们方法的核心是应用于独特的细粒度能源语料库的下一代统计和机器学习方法，以及来自我们合作伙伴的过程数据（英国电信、乐购、兰开斯特大学设施（一个城镇大小的校园），以及能源管理咨询和云能源分析提供商BEST）。这将使我们能够首次将最先进的统计技术应用于这一领域的一个非常重要的数据集。基于统计和机器学习技术对细粒度能源数据的应用，开发用于支持分析、识别和推荐节能策略的自动化技术;2.通过比较建筑物内部和建筑物之间的能源使用差异，了解能源使用的方式、地点和时间，以确定减少和转移需求的机会;3.支持定期和重复的分析，以实现随着时间的推移在能源减少方面的持续改进。提供开源的、许可的实施，以实现吸收，甚至超越我们的项目合作伙伴及其合作伙伴网络。我们的出版和宣传策略将尽量向不同的持份者团体，包括学术界、从业员和主要行业持份者，介绍我们的项目成果。

项目成果

Identifying Metering Hierarchies with Distance Correlation and Dominance Constraints

• DOI: 10.1109/icmla55696.2022.00242
• 发表时间: 2022-12
• 期刊: 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)
• 作者: Tak-Shing T. Chan;A. Gibberd

The sparse dynamic factor model: a regularised quasi-maximum likelihood approach

稀疏动态因子模型：正则化准最大似然方法

• DOI: 10.1007/s11222-023-10378-1
• 发表时间: 2024
• 期刊: Statistics and Computing
• 影响因子: 2.2
• 作者: Mosley L

The real climate and transformative impact of ICT: A critique of estimates, trends, and regulations.

• DOI: 10.1016/j.patter.2021.100340
• 发表时间: 2021-09-10
• 期刊: Patterns (New York, N.Y.)
• 作者: Freitag C;Berners-Lee M;Widdicks K;Knowles B;Blair GS;Friday A
• 通讯作者: Friday A

COVID-19 as an Energy Intervention: Lockdown Insights for HCI

COVID-19 作为一种能源干预措施：HCI 的锁定见解

• DOI: 10.1145/3544549.3585896
• 发表时间: 2023
• 作者: Bremer C

Space temperature policy towards net-zero: recommendations from a systematic review of UK HEI heating policies

• DOI: 10.1080/09613218.2022.2093694
• 发表时间: 2022-07
• 期刊: Building Research & Information
• 影响因子: 3.9
• 作者: Kathy New;Adam Tyler;A. Friday;M. Hazas;Alexandra Gormally