Borusan EnBW Reduced Balancing Costs with Kavaken

We have been working with multiple providers for more than 5 years. Thanks to Kavaken's solutions we have achieved the highest accuracy in our production forecasts in all of our plants. — Tuna Guven, Assistant General Manager, Operations at Borusan EnBW

Kavaken's product enabled us to reduce balancing costs and save on operational expenses. — Emre Okuyan, Assistant General Manager, Business Development, Sales & Trading at Borusan EnBW

Challenge

Borusan EnBW is one of the leading renewable energy providers in Turkey with an installed capacity of 505MW.

• Given their large capacity Borusan EnBW were looking to improve their day ahead predictions to reduce balancing costs and increase operational efficiency
• In addition to accuracy improvement Borusan EnBW are expanding their asset portfolio and need a solution that can scale with growing needs
• On a user level, one of the biggest challenges was the lack of a user interface to monitor predictions with historical data and insights

Solution

Kavaken addressed Borusan EnBW's concerns with a holistic solution powered by its proprietary machine learning platform. First, forecast accuracy was improved via leveraging more data resulting in higher financial returns. Second productivity challenges were addressed with a simple and intuitive customer interface where users can digest key metrics and make informed decisions. Kavaken's close collaboration with Borusan EnBW yielded the best accuracy seen in the last 5 years. Improvements in accuracy were validated across 8 wind farms.

Results

• 5–10% Improvement in forecast accuracy
• Kavaken predicted highest accuracy across all 8 power plants for a whole year
• Solution is easily being scaled to Borusan EnBW's new fields
• Received the Best Corporate Startup Collaboration Award