Smart Energy Optimization
Case Study
Powering the Future with Smart Energy Optimization
Solaris Energy, a leader in renewable power solutions, faced increasing complexity in managing their diverse energy generation portfolio. With a commitment to net-zero emissions, they needed an intelligent system to optimize asset performance, forecast demand accurately, and reduce operational costs while meeting sustainability goals.
Turnover
10 months
Company
Solaris Energy
Industry
Energy
Budget
Confidential
The Challenge of Energy Complexity
Managing multiple energy sources—solar farms, wind turbines, battery storage, and grid connections—created forecasting and optimization challenges. Solaris Energy’s legacy systems required extensive manual input from their data scientists, consuming valuable time and limiting the frequency of optimization runs necessary for maximum efficiency.
Implementing the Databricks Solution
The Solaris data team deployed Databricks’ Data Intelligence Platform to revolutionize their approach to energy management. By transitioning from virtual machines to a unified lakehouse architecture, they created digital twins that accurately simulate asset behavior and predict future demand patterns with unprecedented precision.
Transformative Results
- 92% reduction in forecasting time, from full day processes to under 2 hours weekly
- $420K annual cost savings through optimized energy asset performance
- 35% increase in optimization frequency, enabling weekly instead of monthly analysis
- 18% carbon emission reduction through more efficient combined heat and power systems
- 3x faster implementation cycles for new energy management strategies