In partnership with Norwegian company Think Outside the team developed a solution to forecast the water availability coming from rivers and snowmelt into reservoir lakes as well as electricity prices to optimize the renewable energy production in Scandinavia.

The problem

Think Outside is a Norwegian company currently focused on providing clients with “constant access to accurate and reliable snow data … to make projections you can be confident in”.

They use radar systems to image snowbanks so that they can provide their clients within the hydropower energy industry with data about the density of snowpacks. The hydropower companies can use this data to make better predictions about the future volume of water that will be flowing into reservoirs due to snow melting and from this make more accurate predictions about the amount of energy they will be able to generate from this water as it passes into reservoirs and then through the hydropower electricity turbines.

Think-Outside wished to expand its current data and forecasting offerings so that they are able to provide additional value to its clients in the hydropower industry. The goal of this project was the development of a machine learning pipeline to make predictions for both water inflow into reservoirs/lakes and future electricity prices.

The project outcomes

Due to the different requirements of water inflow and electricity price modeling, the project was divided into two sub-projects: (1) Water inflow prediction and (2) Electricity price prediction. For both sub-projects, we defined the task objectives relating to the project goals specified by Think-Outside, explored numerous data sources, downloaded and processed data (in an automated manner where possible), and then cleaned, explored, and preprocessed the data.

Finally, machine learning models we created and trained on the input datasets and then these models could be used to make predictions about future values of water inflow on a per reservoir/catchment area level and also to predict future values of electricity prices of different energy bidding zones within Norway. The performance of the machine learning models and predictions was analyzed using a number of different error metrics as well as visual representations of the model performances.