Advanced Infrastructure Technology – LAEP apps

Aiming to assess the value of a Local Area Energy Planning (LAEP) using big data analysis to help non-technical energy planners to rapidly model energy scenarios in close to real-time using a novel common analysis framework.

The application should build on the common data architecture from recent data projects – accessing, integrating and exploring the datasets from those projects. It should also integrate open datasets that are currently too dissipated and difficult for planners to explore without expensive specialist support.

The service vision is a web GIS single page application which enables users to view, download/upload, and analyse building-level data in order to plan and optimise the siting of any combination of low carbon technologies at scale.

By maintaining this data in up-to-date and geospatially mapped formats, accessible on web-based visualisation platforms, we remove data-as-a-barrier to LAEP analysis. Better whole-system planning with full stakeholder engagement has been shown to reduce cost of capital by 7% saving the UK taxpayer £1.4 billion annually in infrastructure expenditure.

City Science Corporation – AI generative design tool for low-cost district heating networks

Aiming to build a generative design tool that will automate the optimal design of district heating networks. Currently, design is costly and time-consuming and often lacks sufficiently detailed data to ensure system efficiency. In many cases this leads to over-sizing of equipment which increases capital costs which are ultimately passed on to the end consumer or can make projects unviable.  

• An automated, computer-driven process, will radically reduce the up-front costs of modelling and system sizing; 
• Using the latest AI techniques combined with detailed geographic data will optimise system design to increase the affordability of heat networks and maximise their viability.  

End consumers will benefit through lower heating costs, warmer homes/buildings and reduced fuel poverty. Taking into account multiple design options will allow for greater focus on zero-carbon fuel sources (including hydrogen) which will promote a new generation of true zero-carbon district heat. 

This would help the UK to meet its 2050 vision of meeting 20% of heating demand by heat networks. 

Alian (Farad.ai) – AI for low carbon technology site optimisation

Aiming to model the shift of complex power flows in the electricity network, to act as a central nervous system for the power sector. The platform uses cutting-edge analytics to understand and predict strains on the grid to make use of congested electricity and incorporate more renewable energy.  

The strains on the electricity network are going to be more severe in time as the world starts to deliver their Net-Zero ambition, which means the same power network will have to deliver 3x as much electricity to consumers.  

Farad.ai envisions a future where the energy system will be working in harmony with other utilities, including gas and water networks, to revolutionise the energy sector. 

Mind Foundry – Energy-focused geospatial system using multi-sectoral data to deliver net zero

Aiming to use geospatial data, which combines location information with attribute and often temporal information, to contribute to the evolution of the distributed energy paradigm.  

The ability to analyse sparse or incomplete data, including from smart meters, electric vehicle (EV) uptake, EV charging type, locations and user profile, has a range of implications that are difficult to model with conventional approaches.  

Mind Foundry are building an energy-focused geospatial system that will enable the user to visualise overlays of multivariate spatially and temporally varying data, model and predict trends and correlations, infer across areas of sparse data collection, and model the effects of changes on the system such as varying supply, demand or infrastructure. It will further allow for the simulation and testing of different strategies, for example, alternative charging point placement.

Using world-class expertise in Bayesian optimisation and scalable probabilistic models to significantly improve the accuracy of AI models. The successful application of this approach will enable the local energy sector to be more quantitative and targeted in its planning and prioritising of resources.