Smart grid monitoring
Real-time Data Analysis: CrateDB enables real-time analysis of data from smart meters, sensors, and other grid devices, providing insights into power consumption patterns, grid congestion, and equipment performance.
Predictive Analytics: By leveraging historical data and machine learning algorithms, CrateDB can predict potential grid issues, such as equipment failures or overloads, allowing utilities to proactively address them and prevent costly outages.
Predictive maintenance
Minimized Downtime: CrateDB's predictive maintenance capabilities enable utilities to detect potential equipment failures before they occur, allowing for proactive maintenance scheduling. This minimizes unplanned downtime, ensuring continuous energy supply and optimizing grid reliability.
Extended Asset Lifespan: By identifying maintenance needs based on actual equipment condition rather than fixed schedules, CrateDB helps utilities optimize maintenance activities. This approach reduces unnecessary maintenance costs and extends the lifespan of critical energy infrastructure, maximizing return on investment.
Energy consumption analytics
Granular Data Analysis: CrateDB allows utilities to analyze energy consumption data at a granular level, including individual customer usage patterns, peak demand periods, and trends over time. This detailed analysis enables utilities to identify opportunities for efficiency improvements and demand-side management strategies.
Optimized Resource Allocation: By gaining insights into energy consumption patterns, utilities can optimize resource allocation, such as adjusting generation and distribution strategies to meet changing demand patterns. CrateDB's real-time processing capabilities facilitate timely decision-making, ensuring efficient resource utilization and grid stability.
Case study #1: Gantner Instruments
Gantner Instruments collaborates with the University of Cyprus to operate a state-of-the-art Smart Micro Grid, dedicated to investigating the control capabilities of renewable energy sources in the power grid and propelling the energy transition forward. They leverage CrateDB to analyze the vast amount of data generated in real time, enhancing their processes through machine learning (ML). With CrateDB, they gain access to their extensive data within microseconds at the frontend, ensuring optimal performance.
Key objectives
- Intelligent Power Grid Management.
- Collect and evaluate power grid data quickly and synchronously so that the network can be optimally controlled in real-time to allow for a higher proportion of renewable energies.
Main technical challenges
- View all data in a central console for real-time data virtualization or integrated into existing systems via API.
- Enable AI driven optimizations of the power grid by allowing data to be written back into the backend.
Case study #2: Best.Energy
Key objectives
- Real-time Usage Statistics and Customer Insights.
- Provide real-time monitoring and statistics to customers connecting their IoT sensors to the platform.
Main technical challenges
- Highly concurrent read & write loads from the thousands of sensors and users.
- Combining current customer data with historical behavior data.
- Custom dashboards generating super complex queries.