The installation of photovoltaics today is largely evaluated in terms of quantity and the success of any market stimulation evaluated on the basis of how well the targets are met. This may cause significant problems for the national infrastructure and may lead to significant unnecessary costs for grid stabilisation. However, these factors are sometimes assessed too simplistically. When considering PV in a national context, it is also largely seen as a homogenous swarm of devices, i.e. all of them reacting rather similarly. This does not consider different orientations (system elevation determines the seasonal maximum, system orientation determines the daily maximum) or regional differences in the environmental conditions such as weather fronts passing in a matter of days over the country rather than instantaneously or the North experiencing a different weather front than the South; nationwide smoothing might very well limit the need for power control. Thus the overarching question in this proposal is 'How can we maximise the benefits and limit the costs for UK plc while having a vibrant PV market?'. The work is split into four topical areas (work-packages), which answer the four key questions: - How much PV are we likely to get with different policies and where is it likely to be installed? This will consider different socio-economic drivers, cost curves of PV and work on installation scenarios giving links to likely social background of installations, locations (as in regions) and quantities. - How much energy will this generate when and where? Based on current installations a model for the performance prediction of systems based on their post-code will be developed and validated against existing FIT data and other available monitoring data. A spin-off of this activity will be the widespread investigation of current installations, that will inform any further discussions on subsidy streams, and the potential for detailed condition monitoring with sparse data will be investigated. The model will be connected with the socio-economic drivers to stochastically locate future installations (using GIS and post-code classifiers), and estimate the energy yield for each system and aggregate to generation regions. This means that essentially for every system (which is today in the range of 400000 systems under the FIT) installed an hourly generation needs to be calculated, which will require very complex speed optimisation in the calculations. - How will it impact the infrastructure? Grid simulations will be carried out bottom up as well as top down to see if there are issues either locally or nationally with the proposed installations. This will allow the recommendation of further measures to strengthen infrastructure and will allow a cost-benefit analysis of PV technology to be undertaken. - What feedback will there be? Most policies will have effects on the questions above and thus it is foreseen that a feedback methodology will be created, calculating the costs/benefits for UK plc as well as evaluating likely responses of the policy makers and grid operators. The collaboration between the different groups will be tightly managed, so that the project outcomes interface well. Tools will be generated and made available with non-proprietary data for public use.

When storms cause trees to fall onto power lines, roads and railways this can pose serious threats to human life and disruption to electricity supplies and transport leading to large financial costs to both operators and users of these networks. There is growing evidence that climate change will lead to an increase in storminess in the UK so the problems associated with tree failure are likely to grow. This project aims to use a computerised system for predicting which trees are likely to fall onto powerlines, roads and railways during the types of storms that typically occur in the UK. This will allow the operators of these infrastructure networks to fell those trees that are most likely to fail and cause disruption. The project will make use of newly-developed techniques which employ airborne laser scanners to map trees and measure their key properties, which will improve our ability to estimate the susceptibility of trees to failure. So the outputs of the system will enable the operators of infrastructure networks to pro-actively manage trees in order to improve the resilience of their infrastructure to future storms. The intensity and impacts of storms vary considerably over space and time so it is not possible to manage trees for all possible conditions. Therefore we will develop the tree failure prediction system so that it is able to use short-range weather forecasts (up to 5 days) which are the most reliable predictions of impending storm events. This will enable the system to predict which trees are likely to fail and cause disruptions to infrastructure networks during the forthcoming storm conditions. This information will help the network operators to draw up effective plans for responding to and recovering from storms, e.g. by organising field teams to be in the locations where greatest tree damage is likely to occur so they can remove fallen debris and repair the infrastructure. To be effective our tree failure prediction system will need to operate quickly and repeatedly so it can respond to regular updates in weather forecasts as storms develop. Also it needs to incorporate assessments of the large number of trees which surround the power, road and rail networks in the UK. Therefore, to achieve this, we will make use of the very powerful cloud-based computing technology that is now rapidly developing. The outputs of our system will be conveyed to users via an interactive web page which will support strategic decision-making and a mobile app that will support field teams. Keywords: tree failure, storm, prediction, power supply, road, rail, decision-making, resilience. The following organisations are stakeholders in the project and will form an advisory board to oversee our work: UK Power Networks, Scottish Power, Transport Scotland, Scottish Water, Bluesky International, Atkins Global.

The Office for Artificial Intelligence (AI) estimates that AI could add £232 billion to the UK economy by 2030, increasing productivity in some industries by 30%. However, to be truly transformational, the integration of AI throughout the global economy requires understanding and trust in the AI systems deployed. The super-human ability for decision-making in new AI systems requires huge volumes of data with thousands of variables, dependencies and uncertainties. Unregulated application of uncertified data-driven AI, limited by data bias and a lack of transparency, brings huge risks and necessitates a community-wide change. AI systems of the future must also be able to learn on-the-job to avoid becoming a high-interest credit card of huge technical debt. There is thus a timely and unmet need for a new theory and framework to enable the creation and analysis of data-driven AI systems that are adaptive, resilient, robust, explainable, and certifiable, with provable and practically relevant performance guarantees. This ambitious fellowship, ARaISE, will deliver a radically new framework for the creation of beneficial data-driven AI systems advancing far beyond classical theories by including certifiable robustness and learning in the problem setting. These new theories will enable a formal understanding of the fundamental limits of large-scale data-driven AI, independent of the application area and learning algorithms. This will enable AI practitioners, through understanding such limitations, to influence policy and prevent incidents before they occur. By connecting different and disparate areas of AI and Machine Learning, working with a world-class team of experts, and by engaging with stakeholders across strategic UK industries and sectors (Healthcare, Manufacturing, Space and Earth Observation, Smart Materials, and Security), ARaISE will create high-value, trustworthy, transformative and responsible AI, capable of reliably 'learning on-the-job' from humans to guarantee capability and trust. Novel human-centric AI, designed to function for the benefit of society, will complement and connect to existing work in the AI research arena, enabling co-development with project partners and focus on strategic industry challenges to ensure real-world relevance is built into research programme and its outputs, facilitating capacity and capability growth. ARaISE will generate gold standard tools for tasks that are currently heavily reliant upon human input and will support long-term global transformation. Impact and knowledge exchange activities, embedded throughout this programme of work, will support uptake of developed novel AI systems and, through leadership and ambassadorial activities, will support a step-change in how AI systems are built and maintained to ensure resilient, robust, adaptive and trustworthy operation. The inclusive research programme has been designed to support the career development of the project team and wider stakeholder group maximising the potential for flexible career paths whilst maintaining flexibility to creatively support the team to develop exciting new technology with real world relevance and guide future AI research. The issues of AI and ethics underpin the programme with responsible research and innovation embedded throughout its activities. Raising public and AI practitioners' awareness, and ultimately influencing policy by active engagement with the UK and AI ethics expertise and policymakers, will ensure that the outcomes are socially beneficial, ethical, trusted and deployable in real world situations. Planned engagement with the ATI, CDTs, partners, and their networks, the development of new partnerships, methodologies and applications, will encourage links between these organisations, build UK expertise, skills and capacity in AI and contribute to realising government investment in UK Societal Challenges and ensure that the UK remains at the forefront of the AI revolution.

The Office for Artificial Intelligence (AI) estimates that AI could add £232 billion to the UK economy by 2030, increasing productivity in some industries by 30%. However, to be truly transformational, the integration of AI throughout the global economy requires understanding and trust in the AI systems deployed. The super-human ability for decision-making in new AI systems requires huge volumes of data with thousands of variables, dependencies and uncertainties. Unregulated application of uncertified data-driven AI, limited by data bias and a lack of transparency, brings huge risks and necessitates a community-wide change. AI systems of the future must also be able to learn on-the-job to avoid becoming a high-interest credit card of huge technical debt. There is thus a timely and unmet need for a new theory and framework to enable the creation and analysis of data-driven AI systems that are adaptive, resilient, robust, explainable, and certifiable, with provable and practically relevant performance guarantees. This ambitious fellowship, ARaISE, will deliver a radically new framework for the creation of beneficial data-driven AI systems advancing far beyond classical theories by including certifiable robustness and learning in the problem setting. These new theories will enable a formal understanding of the fundamental limits of large-scale data-driven AI, independent of the application area and learning algorithms. This will enable AI practitioners, through understanding such limitations, to influence policy and prevent incidents before they occur. By connecting different and disparate areas of AI and Machine Learning, working with a world-class team of experts, and by engaging with stakeholders across strategic UK industries and sectors (Healthcare, Manufacturing, Space and Earth Observation, Smart Materials, and Security), ARaISE will create high-value, trustworthy, transformative and responsible AI, capable of reliably 'learning on-the-job' from humans to guarantee capability and trust. Novel human-centric AI, designed to function for the benefit of society, will complement and connect to existing work in the AI research arena, enabling co-development with project partners and focus on strategic industry challenges to ensure real-world relevance is built into research programme and its outputs, facilitating capacity and capability growth. ARaISE will generate gold standard tools for tasks that are currently heavily reliant upon human input and will support long-term global transformation. Impact and knowledge exchange activities, embedded throughout this programme of work, will support uptake of developed novel AI systems and, through leadership and ambassadorial activities, will support a step-change in how AI systems are built and maintained to ensure resilient, robust, adaptive and trustworthy operation. The inclusive research programme has been designed to support the career development of the project team and wider stakeholder group maximising the potential for flexible career paths whilst maintaining flexibility to creatively support the team to develop exciting new technology with real world relevance and guide future AI research. The issues of AI and ethics underpin the programme with responsible research and innovation embedded throughout its activities. Raising public and AI practitioners' awareness, and ultimately influencing policy by active engagement with the UK and AI ethics expertise and policymakers, will ensure that the outcomes are socially beneficial, ethical, trusted and deployable in real world situations. Planned engagement with the ATI, CDTs, partners, and their networks, the development of new partnerships, methodologies and applications, will encourage links between these organisations, build UK expertise, skills and capacity in AI and contribute to realising government investment in UK Societal Challenges and ensure that the UK remains at the forefront of the AI revolution.