The UK's carbon targets, as defined by the Climate Change Act of 2008, specify an emissions reduction of 80% by 2050, which the government has recently revised down to 'net zero' for the same year. In 2017, 17% of the UK's carbon emissions were associated with non-electric use in the residential sector (64.1 Mt CO2), the majority of which were associated with natural gas space heating, cooking and domestic hot water. The UK must therefore decarbonise residential heat to be able to meet its climate change targets, but, in combination with electric vehicles (EVs), this could lead to a 200-300% increase in the UK's annual electricity demand. In terms of deployment at scale, Air Source Heat Pumps (ASHP) operating either in isolation or as a hybrid gas system appear a key technology as they are not site specific and are applicable to both new build housing and retrofit. The UK's low voltage (LV) electricity network will not however, be able to operate with unconstrained electrical heating or EV charging loads. Both loads must be deferrable or scheduled in a manner to support the electricity network and maintain substations and feeders within limits. Household electric heating has the potential to operate as a significant deferrable load which LATENT is seeking to understand and harness. This can provide benefits across scales, namely to the UK (energy security and carbon targets), DNO (Distributed Network Operator as grid support), heat pump suppliers (by demonstrating added grid value), householders (in terms of bill reduction and avoidance of peaking dynamic tariffs) and electricity suppliers by applying aggregation techniques to minimise energy service costs. The key aim of LATENT therefore, is to be able to predict the impact of customers with electrical heating (predominantly ASHP) operating with 3rd party deferrable heating control on the LV network at the feeder / substation level. 3rd party control in this context would be through the energy service supplier, with whom, unlike the DNO, a household has an existing financial contract relationship. LATENT will inform industry of the potential of 3rd party control of deferrable heat through a rigorous field experiment, and, in doing so, accelerate the transition to decarbonised household heating. LATENT will determine the influence of householder personality trait (OCEAN traits: either positive / negative as Openness, Conscientiousness, Extraversion, Agreeableness, Neuroticism) alongside more traditional Census metrics such as educational attainment, house type etc to deliver a multi-variate regression model to describe deferrable heat reduction at the household level. A substation or feeder can then be analysed in terms of its household type mix (10% C+ detached, 30% E- flat etc) to produce a composite substation level, deferrable heat reduction estimate. This model will be realised through field trials with LATENT's industrial partner, Igloo Energy. Igloo have a customer base with smart heating systems and ASHP which support remote 3rd party control. LATENT will test (i) householder's stated acceptance to deferral of heating (in terms of temperature drop and duration) through focus groups and surveys, (ii) actual acceptance of heat deferral through heating season field trials, and (iii) operation of a commercial deferrable heat tariff with a sample of Igloo's customer base.

Our mission is to train the next generations of innovators in responsible, data-driven and knowledge-intensive human-in-the-loop AI systems. Our innovative, cohort-based training programme will deliver cohorts of highly trained PhD graduates with the skills to design and implement complex interactive AI pipelines solving societally important problems in responsible ways. While fully autonomous artificial intelligence dominates today's headlines in the form of self-driving cars and human-level game play, the key AI challenges of tomorrow are posed by the need for interactive knowledge-intensive systems in which the human plays an essential role, be it as an end-user providing relevant case-specific knowledge or interrogating the system, an operator requiring crucial information to be presented in an intelligible form, a supervisor requiring confirmation that the system's performance remains within acceptable limits, or a regulator assessing to what extent the system operates according to exacting standards concerning transparency, accountability and fairness. Each of these examples demonstrates a need for specific and meaningful interaction between the AI system and human(s). The examples also demonstrate the importance of knowledge for achieving human-level interaction, in addition to the data driving the machine learning aspect of the system. In close conversation with our industry partners we thus identified Interactive Artificial Intelligence (IAI) as a core sub-discipline of AI where the need for and deficit in advanced AI skills is abundantly evident while being homogeneous enough to have intellectual integrity and be taught and researched within the context of a single CDT. The most important aspects of the training programme are: - Knowledge-Driven AI and Data-Driven AI are core components treated in a close symbiotic relationship: the former uses knowledge in processes such as reasoning, argumentation and dialogue, but in such a way that data is treated as a first-class citizen; the latter starts from data but emphasises knowledge-intensive forms of machine learning such as relational learning which take knowledge as an additional input. - Human-AI Interaction is another core component addressing all human-in-the-loop aspects, overseen by a co-investigator from the human-computer interaction field. - Responsible AI is underpinning not just the taught first year but the students' doctoral training throughout all four years, overseen by two dedicated co-investigators with backgrounds in IT law and industrial codes of practice. Other skill requirements from stakeholders include: the ability to design and implement complete end-to-end systems; acquiring depth in some AI-related subjects without sacrificing breadth; the ability to work in teams of people with diverse skill sets; and being able to take on a role as "AI ambassadors" who are able to inspire but also to manage expectations through their in-depth understanding of the strengths and weaknesses of different AI techniques. The IAI training programme is designed to achieve this by strongly emphasising cohort-based training. Students will develop their projects and coursework within an innovative software environment which means easy integration of their work with that of others. This virtual hub is complemented by a physical hub where all cohorts are colocated -- together both hubs will strongly promote interaction both within and between cohorts: e.g., projects can aim at improving or extending software produced by the previous cohort, so that senior students can be involved in mentoring their juniors. In summary, the IAI training programme pulls together Bristol's unique and comprehensive strengths in doctoral training and AI to deliver highly trained AI innovators, equipping them with essential skills to deliver the interactive AI technology society requires to deal with current and future challenges.

This proposal responds to a call from the Research Councils for a national Centre on energy demand research, building on the work of the existing six End Use Energy Demand Centres, for which funding ends in April 2018. Energy demand reduction is a UK success story, with a 15% fall in final energy consumption since 2004. Major further reductions are possible and will be needed, as part of a transformation of the energy system to low carbon, to deliver the goals of the Paris Agreement and the UK carbon budgets. Moreover, a low carbon energy system will be increasingly reliant upon inflexible and variable electricity generation, and therefore demand will also need to become more flexible. In short, changes in energy demand reduction will need to go further and faster, and demand will need to become more flexible. These challenges have far-reaching implications for technology, business models, social practices and policy. Our vision is for energy demand research in the UK to rise to these challenges. The Centre's ambition is to lead whole systems work on energy demand in the UK, collaborating with a wider community both at home and internationally. We aim to deliver globally leading research on energy demand, to secure much greater impact for energy demand research and to champion the importance of energy demand for delivering environmental, social and economic goals. Our research programme is inter-disciplinary, recognising that technical and social change are inter-dependent and co-evolve. It is organised into six Themes. Three of these address specific issues in the major sectors of energy use, namely: buildings, transport and industry. The remaining three address more cross-cutting issues that drive changing patterns of demand, namely the potential for increased flexibility, the impact of digital technologies, and energy policy and governance. Each Theme has a research programme that has been developed with key stakeholders and will provide the capacity for the Centre to inform debate, deliver impact and share knowledge in its specific area of work. The Themes will also undertake collaborative work, with our first joint task being to assess the role of energy demand in delivering the objectives of the UK Government's Clean Growth Plan. The Centre will also include Challenges that respond to cross-thematic questions for UK energy demand. These will mostly be developed in consultation over the early years of the Centre, and therefore only one is included in the initial plan: on the decarbonisation of heat. The Centre will function as a national focus for inter-disciplinary research on energy demand. In doing this it will need to respond to a rapidly evolving energy landscape. It will therefore retain 25% of its funds to allocate during the lifetime of the Centre through a transparent governance process. These funds will support further challenges and a 'Flexible Fund', which will be used to support research on emerging research questions, in particular through support for early career researchers. We are working closely with key stakeholders in business and policy to design our research programme and we plan detailed knowledge exchange activities to ensure that the work of the UK energy demand research community has broader societal impact.

Addressing climate change through reducing carbon emissions is a crucial international goal. End use energy demand (EUED) reduction is essential for the UK to meet its legally binding 80% carbon reduction target and has significant economic and social benefits: it lowers the operating costs of businesses, increasing their competitiveness, and reduces the fuel bills for home owners, guarding against fuel poverty and improving quality of life. Government, industry and academia recognise the importance of EUED reduction and are responding by developing new policies, products and services. However, there is a shortage of highly trained individuals who will spearhead these initiatives. Recognising this, the Engineering and Physical Science Research Council (EPSRC) has identified EUED in buildings, transport and industry as a priority funding area for the development of a Centre for Doctoral Training (CDT). For the last 4 years, the UCL Energy Institute and the School of Civil and Building Engineering at Loughborough, have run a successful CDT: the London-Loughborough Centre for Doctoral Research in Energy Demand (LoLo). The Centre is seeking funding for a further 8 years to train 60 students. The scope will be expanded beyond buildings to include energy demand in transport and industry directly related to the built environment. The new Centre will build on the existing four year programme: a one year Masters of Research in Energy Demand followed by a three year PhD. Training will be enhanced by an annual colloquium; international summer school; team building away days; seminar series'; creativity, communication and business training; and numerous other activities. Students will undertake placements with partners and in relevant overseas organisations. They will have a firm grounding in core skills and knowledge, but appreciate the multi-disciplinary perspective needed to understand the technical, economic and social factors that shape energy demand. The Centre's research will address new challenges within five themes, grouped around major research programmes: technology and systems, energy epidemiology, urban scale energy demand, building performance and process, and unintended consequences. This linkage ensures students' work gains momentum, is at the forefront of knowledge, has excellent resources, and is supported by a wide group of world class academics. The Centre will again be led by Profs Lowe and Lomas; together they have over 60 years of experience in energy and buildings. They will be supported by Academic Managers and Administrators and over 40 academic supervisors whose expertise spans the full range of disciplines necessary for EUED research: from science and engineering to ergonomics and design, psychology and sociology through to economics and politics. An Advisory Board will help steer the Centre, whilst the wider group of 26 partners, representing policy, industry, academia and NGO interests, will aid students' training by: developing projects, offering mentoring, hosting students in their organisation, giving workshops and seminars, and direct funding. The proposed new Centre represents excellent value for money. The total cost to the EPSRC to train 60 students is less than the current Centre cost to train 40 students. However, the funding per student will rise by 20%, a result of the financial commitment of our partners and host institutions. The Centre aims to have an enduring impact through our graduates and their research. Short term impact will be achieved through students' engagement with industry, policy makers, NGOs and academia through the annual Colloquium, the international summer school, publications, the web-site and other social media, working with partners and through public engagement. In the long term our graduates will help transform the EUED sector through projects they lead, the students and colleagues they will train and the organisations they influence.

Deep changes are happening in the supply side of energy systems. The UK has halved carbon emissions from electricity system from over 150 million tonnes in 2012 to under 70 in 2018 and China is adding about 20 GW of wind generation capacity per year and has replaced all buses in the city of Shenzhen with electric busses. Very clearly there is much more to do: the remaining decarbonisation of electricity, the electrification of other sectors and sourcing alternative, zero-carbon fuels. Cities have traditionally been huge consumers of energy brought in from their hinterland and yet load growth in energy networks is inevitable as more services, notably transport, are decarbonised through electrification and building density increases through re-development of with taller buildings. The traditional response to this, adding more plant and equipment, is recognised as being an inefficient. An interesting trend is the emergence of Local Energy Systems (LES) and Multi-Energy Micro-Grids (MEMG). LES and MEMG are a means for raising self-consumption of local energy resources; tapping into sources of flexibility in how the services derived from energy; using local services for both local and national control and moving to a smart ways of ensuring resilience. The recent power outage in the UK (9/8/19) highlighted that transport systems and other urban infrastructure are particularly vulnerable. A re-imagining of how resilience is provided in the urban setting could hugely reduce that vulnerability. Despite the differences between the histories and geographies of cities in China and the UK, we find common challenges and a complementary set of research expertise. This project brings together experts in power electronics, optimisation, control and fault-management from UK and China. Existing energy networks, especially electricity networks, were designed assuming power enters a city from remote power stations and the network inside the city distributes this. This led to a radial set of lines spreading out from substations. This structure is unable to support the formation of flexible microgrids around local generation and storage resources. We propose to re-structure the legacy networks using power electronics devices that give controlled power flows between previously unconnected networks points. This opens up dynamically restructuring the power flow in urban areas to allow greater local self-consumption of energy, for instance moving solar power residential properties to work-place charging of electric vehicles. It also allows islands to be formed in reaction to power cuts that keep essential services running while placing non-essential services on hold. We also look at hardware and control issues. The hardware for electronic routing of power has been discussed in principle but it is too large and not efficient enough to be used in urban settings. We will work on new forms of modular power converter that raise efficiency, reduce physical volume and provide resilience to component failures. Control systems for energy networks are centralised: they gather data from across large areas, make decisions and then issue commands. The microgrid concept changes this to a decentralised approach. A key benefit of decentralisation is the ready access to information about flexibility in energy consumption, e.g which electrical vehicles could delay charging or might supply power to aid with a power cut. Local control also gives opportunities to run the heat/cooling of buildings, the transport energy system and the electricity system as an integrated whole. This can lead better integration of renewable energy and therefore deep decarbonisation but requires a major step forward in managing uncertainty over the local energy resources and demands. We will bring the techniques of stochastic optimisation and machine learning to bear on this problem and devise a control and operations framework for smart urban energy systems.