Reducing End Use Energy Demand in Commercial Settings Through Digital Innovation

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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.