Smith Institute has been awarded Most Innovative Use of AI (Enabler) for its work supporting Scottish and South Energy Networks to create an explainable AI solution to identify factors that can lead to energy poverty across the UK. This project is vital given the vast changes and innovations occurring within the energy sector and it is essential that vulnerable members of society are not abandoned.
The Smith Institute Vulnerability Future Energy Scenarios project identifies key vulnerability factors and drivers to provide a map of currently and potentially vulnerable areas. This enhanced intelligence helps decision makers understand how their plans could impact vulnerable and potentially vulnerable customers.
Promoting fairness and inclusivity in a renewable energy world
The energy sector is undergoing enormous change driven by the rise of renewable generation, the move towards a net zero carbon grid, and escalating energy prices. As network operators make significant investments to upgrade their grids to support low carbon technologies, a vital question has arisen: how can businesses design a fair and just transition, making sure vulnerable customers are not disadvantaged or left behind?
In a pioneering collaboration funded by the Network Innovation Allowance, Scottish and Southern Electricity Networks (SSEN) joined forces with Smith Institute, Imperial College London, and National Energy Action to develop a first-of-a-kind future energy scenario – the Vulnerability Future Energy Scenario (VFES) – which helps operators understand what makes customers and communities more or less resilient, where vulnerability and fuel poverty are most prevalent and which factors will drive change in the coming years.
SSEN is a forward-thinking distribution network operator that maintains and operates the electricity distribution infrastructure for 3.8 million customers across the north of Scotland and central southern England.
SSEN leadership recognised the challenges presented by the transition to net-zero, particularly for vulnerable customers who rely heavily on secure, affordable, and reliable electrical supply. Aware that their existing scenarios did not consider consumer vulnerability, SSEN aimed to support vulnerable customers and prevent new forms of vulnerability when making infrastructure and operational planning decisions. SSEN entrusted Smith Institute with the task of harnessing their extensive data resources and employing machine learning to identify the factors driving energy vulnerability along with their impact.
The effort was complicated by the diversity of SSEN’s 3.5 million household customer base. Various driver groups interact in intricate ways to impact vulnerability, which cannot be simply attributed to a single factor like age. The team therefore needed to identify each of the groups that exist and determine how their demographic features drive vulnerability. It was important to make sure that these findings were explainable and interpretable to decision-makers, making black box models unsuitable.
In collaboration with SSEN, Smith Institute developed an explainable AI solution to identify energy vulnerability drivers and their influence on different regions. Using advanced techniques to incorporate explainability into the model, the team were able to capture complex, non-trivial relationships that exist in the data while retaining the ability to interpret what the model learns.
The model used a range of demographic features including metrics related to population age, health benefits, income, social isolation, internet usage, housing, and qualification to predict vulnerability across SSEN’s serviced areas. It generated predictions and explanations for each area’s vulnerability, detailing how factors combined to influence it. Areas were then grouped with similar vulnerability drivers, applying data-driven clustering which helped reflect natural patterns.
To overcome high-dimensional data challenges, the data team reduced dimensionality before clustering, resulting in more representative groups. Through this process, key vulnerability drivers were identified and insights presented into SSEN’s customer base, providing SSEN with better decision intelligence on the scale and location of such situations as well as what new situations may cause vulnerability.
Smith Institute’s Explainable AI solution ultimately provided SSEN with a detailed understanding of their most vulnerable customers and the factors driving their vulnerability. Having identified the distinct groups of areas with similar drivers of vulnerability, SSEN can now take steps to ensure future investment strategies are designed appropriately to provide a just transition of the energy network for all.
Localised and targeted investments and interventions can be made to address the specific drivers of vulnerability in each location, and strategies can be shared across different locations where similar drivers of vulnerability are dominant.
As a result of this project, SSEN is embedding Vulnerability Future Energy Scenarios into its Distribution Future Energy Scenarios process, ensuring that the network takes vulnerable customers into account as it makes million- and billion-pound infrastructure investments for the future.
