Predict 4 Resilience (P4R) will provide accurate fault insights and forecasts for its users during adverse weather events. By predicting both where network faults are most likely to occur and their expected volume up to 5 days in advance, networks can pre-emptively allocate resources and materials to the right locations ahead of time.
The ambition and expectation is that the end software solution will be fit for all GB and international DNO’s, as well as any adjacent sectors who suffer weather related interruptions.
Project Objectives
Through an advanced indication of where inclement weather will affect the network and a better prediction of expected fault numbers, P4R will enable resources (engineers, mobile generation, welfare provisions, customer liaison staff, mobile catering for consumers etc) to be proactively placed in those areas most likely to be impacted, something that is especially important in island locations where travel distances are significant.
Benefits for the customer
This is expected to bring forward the travel time to those faults where an onsite presence is required, enabling power supply to be restored sooner than is currently possible. This creates a more resilient network, minimises disruption for customers and brings about financial, social and environmental benefits.
- Restoration of power supply sooner
- Better advanced information regarding potential outages and the ability of the DNO to provide storm warnings to customers in advance.
- Better customer satisfaction through better communications and an earlier restoration time
- A reduction in stress for vulnerable customers and minimising the inconvenience of a disruption in power supply to all customers
- A reduction in CO2 emissions and an improvement in air quality
 | Lead funding License and project manager. |
 | Collaborating network ensuring scalability and applicability to other regions. |
 | Providing expertise in meteorology and statistics. |
 | Building the infrastructure that supports the solution. |
Summary
Our discovery phase will take our existing network data sets, coupled with weather data supplied by the Met Office and assess if this data is sufficient to support the project aims, or identify the gap to realise the required format and volume.
Stage Milestones
The discovery phase demonstrated the potential benefits of the solution. In Alpha, the project utilized data science capabilities and experience in software development to further refine the prototype, resulting in a fault forecast engine which uses cutting edge statistical methods and an interface that closely meets the user needs.
Summary
Our Predict4Resilience (P4R) Alpha phase will look to prototype a "weather fault prediction tool" including the following:
- Prototype the Fault Forecasting Engine which is the fault predicting statistical model using weather forecasts and historical data, such as weather variables and satellite imagery.
- Carry out further user engagement with other DNOs and potentially other infrastructure operators to capture wider user needs and ensure the project continues in a direction to be commercialised and maximise uptake and value.
- Build Wireframes/Mock-Ups of the user interface to inform the Beta Phase design and development.
- Write a blueprint which evolves along the implementation of Agile Methodology to guarantee a fast start at Beta Phase.
- Develop a refined business case incorporating additional user needs from wider user engagement.
Stage Milestones
In Alpha, a prototype fault forecasting model and accompanying data infrastructure was implemented which verified the feasibility of the P4R’s innovations. These statistical models have shown forecast are highly accurate in days 1-7, a key timescale for operational planning for the control room. It was also found that the model successfully predicted severe weather events resulting in large numbers of faults.
Summary
Following the successful demonstration of the prototype’s efficacy, Beta will evolve the prototype into a commercial solution that can be rolled out across GB DNOs and beyond, improving resilience with data-driven fault forecasting and decision-support.
Stage Milestones
Fault forecasting methodology has been productionised and live trials are under way. Enhancements continue to be developed and rolled out to trial participants. Trials will continue until Summer 2026, validating the platform’s performance during normal operations and extreme weather events. Commercialisation strategy is under development.
Key Achievements
Automatic Live Forecasts: Successfully automated live weather forecasts and fault forecasts, enhancing predictive capabilities.
Multi-model Ensemble: Implemented in July 2024, improving forecast accuracy and platform resilience.
Approval for Business Trials:
Secured approval for Business Live Trials, enabling real-world platform testing.