Volta: Value and Feasibility Analysis for Input Data Models
Project summary
Adaptive models are unsupervised machine learning models that can adapt their output and training data to accommodate new data. This prevents the need to manually retrain models based on static data and increases scalability and reduces resource for tagging and sorting data.
This project aims to assess the effectiveness, practicality, and benefits of implementing the adaptive data models within our operations, focusing on its impact on system optimisation and decision-making processes. The input data model will address several identified gaps in our current capabilities, aiming to enhance forecasting, optimisation, and situational awareness in various operational scenarios.
Benefits
This feasibility study hopes to discover the time and resource savings that could be achieved when it comes to developing adaptive models of different components. There is an expectation that adaptive models will significantly improve efficiency and performance of optimisation solvers by being able to retrain themselves on updated data as it is added to the operational databases. This will add a new capability to NESO and allow for the development of scenarios while providing an envelope of outputs rather than deterministic results with only one output.
| Name | Status | Project reference number | Start date | Proposed End date | Expenditure |
|---|---|---|---|---|---|
Volta – Value and Feasibility Analysis for Input Data Models |
Live | NIA2_NESO108 | Mar 2025 | Sept 2025 | £670,000 |
Adaptive models are unsupervised machine learning models that can adapt their output and training data to accommodate new data. This prevents the need to manually retrain models based on static data and increases scalability and reduces resource for tagging and sorting data.
This project aims to assess the effectiveness, practicality, and benefits of implementing the adaptive data models within our operations, focusing on its impact on system optimisation and decision-making processes. The input data model will address several identified gaps in our current capabilities, aiming to enhance forecasting, optimisation, and situational awareness in various operational scenarios.
Benefits
This feasibility study hopes to discover the time and resource savings that could be achieved when it comes to developing adaptive models of different components. There is an expectation that adaptive models will significantly improve efficiency and performance of optimisation solvers by being able to retrain themselves on updated data as it is added to the operational databases. This will add a new capability to NESO and allow for the development of scenarios while providing an envelope of outputs rather than deterministic results with only one output.
| Name | Published |
|---|---|
| NIA Project Registration and PEA Document | 28 Aug 2025 |
| Annual Progress Report | 28 Aug 2025 |
Outcomes
The project is ongoing. The outcomes to date include:
- The differentiation between adaptive inputs into two categories: the forecasting layer and the enrichment layer. The forecasting layer includes adaptive models, which utilise machine learning to forecast inputs required for decision making, such as generation, demand and interconnector flow. The enrichment layer includes the system requirements and the model of the transmission system, which it is suggested could be rule-based models.
- The data input assessment model identifies the necessity for adaptive input models. It highlights data streams which, if enhanced, could benefit future adaptive input models.
Lessons Learnt
The lessons learnt at this stage are:
- Data requests for projects of this length can be challenging if the relevant data is not publicly available. Mitigation strategies such as early data access requests, utilising data already in DAP, minimising data requests to that necessary for the case study / minimal viable product