Learnings

Outcomes

The project is currently halfway through delivery. The initial work package, to build models improving on the carbon intensity of a power plant compared to the current single value, has built initial models for gas, coal and biomass, and compared them to the single values for carbon intensity currently used.  These models show reasonable initial performance given the small amount of available data and are being refined to ensure best performance and to reduce the risk of overfitting.  The models are built using features generated from the half hourly running of the power plants. Various iterations and combinations of the features are used as inputs, including considering interactions and nonlinear terms.   Due to the low data volumes and a focus on explainability, the modelling approach being used is linear regression and only a small number of features are used in the final models to reduce the risk of overfitting.   
 
The second work package around grid optimisation based on carbon intensity minimisation is due to start in July.    

Lessons Learnt

The key lesson learnt from the first half of this project is that the currently available data (monthly and grouped by power plants), limit the modelling techniques available for understanding the performance of the power plants. This would be improved by collecting this data at more regular intervals, for example daily or weekly, or for single plants.