Revenue efficiency in energy: A data-driven approach with Databricks

Our client, a global leader in renewable energy, partnered with us to enhance their energy pricing capabilities using Databricks. Focused on sustainability, they sought to improve revenue efficiency by predicting energy trends through a dynamic, data-driven approach. We partnered with the Director of Data Operations by identifying and ingesting essential data into Databricks, crucial for building a complex data science model. Despite challenges with limited data, we developed tools to handle future data, ultimately providing them with greater clarity in pricing and financial decision-making.

Client Snapshot

Who:

End-to-end energy development firm

Industry:

Energy

Stakeholders:

Director of Data Operations

Technology used:

Databricks & Azure Data Lakes

Client Background

Our client, a global leader in renewable energy, focuses on the development, ownership, and operation of wind, solar, and transmission assets. Committed to sustainability, they work to deliver innovative, clean energy solutions that support a greener future. With an expansive portfolio of operational and development projects, they consistently prioritize environmental stewardship while advancing renewable energy initiatives worldwide.

The challenge

The client’s operations department houses a data Center of Excellence (COE) that supports key business groups, including the energy management team responsible for pricing energy. Though equipped with various tools, they looked for ways to leverage Databricks for better energy price predictions. This innovation project aimed to shift from simply forecasting next-day energy prices to a more dynamic approach, considering historical data, weather, load, and supply-demand factors. One challenge we encountered was the lack of sufficient data in certain areas to make the model more robust. Since this was an innovation project, it was understandable that the data wasn’t fully available yet.

The solution

Their primary goal was to enhance revenue efficiency by predicting trends. We equipped their data management team with a more intuitive approach to data processing, enabling more informed decisions. Our support included identifying and facilitating the ingestion of data into Databricks, essential for building a complex data science model. Given the lack of sufficient data in some areas, we also developed the necessary tools to ingest future data as it becomes available.

Impact

Now, our client has a clear understanding of energy pricing and how to secure funds from banks for their energy needs. Blueprint’s swift implementation provided significant value, saving months of effort and giving our client a strong head start on future in-house projects. We also provided detailed suggestions on developing pricing models, thorough documentation, and multiple code fragments to guide their continued development. These resources position our client to move forward confidently with their innovation project and enhance their decision-making processes.

“The Blueprint team has been a force multiplier for our small data team allowing us to focus more on the subject matter rather than the data infrastructure. Because they’ve been able to move us quickly from concept to a useable workflow, we were able to utilize their work as templates for many future projects. We consider Blueprint to be an instrumental partner when we’re considering standing up new projects.”

Senior Manager, Data Center of Excellence

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