Learnings

Outcomes

The project has delivered a model which allows the ESO to forecast the Norwegian electricity price at an hourly resolution. A series of models were developed and the best modelling technique was the LSTM modelling package. Although, thorough testing is ongoing, indications suggest some improvements could be made by considering some periodic elements in the model.  
 
The project highlighted that the relationship between forecast prices and the intra-day prices are greatly influenced by external events. These insights provide excellent opportunities for model improvement and future study. 
 
The project helped us to narrow down the options for future models. The techniques described, used and analysis have allowed NGESO to rule out many modeling approaches, allowing resources to be efficiently deployed in productive areas.  

Lessons Learnt

Working with cutting edge AI and Machine Learning datasets can bring fresh approaches to modeling problems. Using this external expertise to apply novel modeling methods allows a faster transition to alternative approaches in the ESO and the wider energy industry. For example, the LSTM PoC model is something we could apply to other time series forecasting projects within the ESO. 
 
Additionally, working with market leaders in the field allowed ESO to learn key skills for ensuring the smooth running of data science projects. This includes new methods for logging data and how it can be efficiently stored.  
 
The project also helped understand the limits of modelling in predicting price futures. Future projects may benefit from a greater understanding of the specific market and from stakeholder and SME interaction as there are limits to insights that can be gained from a data only approach.