Reducing End Use Energy Demand in Commercial Settings Through Digital Innovation

英国、爱尔兰、加拿大和法国都宣布进入气候紧急状态。气候变化从未像现在这样受到公众的关注。法律承诺到2050年将温室气体排放量相对于1990年的水平至少减少80%，尽我们所能减少能源需求从未像现在这样重要。这个项目的承诺是帮助提供新的方法来分析能源和建筑系统数据（来自物联网设备）的“数据洪流”，这些数据可以帮助解锁能源效率，并在嘈杂和异构数据的情况下确定能源效率措施的好处；让它变得便宜，可重复，并在持续的基础上进行。我们方法的关键是新颖的统计和混合方法技术，与我们的项目合作伙伴及其数据密切合作，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，另一个类似的建筑物，甚至类似的业务）。这将使节能的“数字化复制”成为可能，甚至使知识和技术在各个部门之间传播开来，具有巨大的潜力。目前，对于许多组织来说，理解这种丰富的信息来源是对可用人力资源进行分析并从可用的潜在见解中获利的挑战。这种分析目前是专业咨询提供商的领域，因为识别数据中的机会需要大量的成本、时间和专业知识。这限制了数据驱动监测和节能战略的渗透，以及不同地点和企业之间知识转移的机会。这个项目将清除这个分析瓶颈。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。主要目标是使商业场所能够减少其能源需求并保持在较低水平，而不需要能源分析人员单独手动调查每个站点，从而增加费用。我们方法的核心是下一代统计和机器学习方法，这些方法应用于来自我们合作伙伴（英国电信、乐购、兰开斯特大学设施（一个城镇大小的校园）、能源管理咨询和云能源分析提供商BEST）的独特的细粒度能源和过程数据语料库。这将使我们第一次能够将最先进的统计技术应用于该领域非常重要的数据集。更具体地说，我们的主要目标是：1。1 .基于统计和机器学习技术对细粒度能源数据的应用，开发支持分析、识别和推荐节能策略的自动化技术；2 .了解能源使用的方式、地点和时间，通过比较房屋内部和房屋之间的能源使用差异，找出减少和改变需求的机会；支持定期和重复的分析，以实现持续的节能改进。提供开源的、允许许可的实现，以支持吸收，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使我们的项目成果最大限度地暴露给不同的利益相关者团体，包括学术界、从业人员和主要的行业利益相关者。

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.