TDengine for
Renewable Energy
Increase production and reduce TCO with the only time-series database designed for IIoT
TDengine provides a unified platform for high-performance ingestion and efficient storage of cross-site data, enabling modern use cases such as predictive maintenance and condition monitoring.
Powering Industrial Big Data for Clean Energy Operators
TDengine enables solar farm applications like performance monitoring, energy forecasting, and solar tracker optimization.
TDengine enables wind farm applications like operational optimization, predictive maintenance, and asset management.
TDengine enables BESS applications like EMS optimization, grid stability, and energy arbitrage.
Data Challenges for Renewable Energy Operators
Sites generate enormous amounts of time-series data from numerous sensors and monitoring devices.
The massive datasets that power next-generation applications take up huge amounts of disk space.
Different sites may run on different systems and protocols, making global analytics an arduous task.
Applications such as condition monitoring need real-time analysis of streaming data from sensors.
How TDengine Can Help
Ingest and store petabytes of data from billions of sensors with no performance deterioration.
Compress your datasets to 1/10 of their original sizes and reduce overhead with tiered storage.
Centralize data from all sites and platforms in our unified system through zero-code connectors.
TDengine supports real-time data processing, allowing for immediate analysis and decision-making.
What Our Customers Say
Solar Power
- Fast pace of innovation, with new features constantly being added
- Queries processed faster than other similar solutions
- Fine-grained data sharing streamlined database admin operations
Nevados produces a single-axis solar tracker adopted for non-flat terrain that is deployed at utility-scale projects across the United States. The previous time-series data stack at Nevados consisted of AWS IoT Core for device interconnection and Telegraf for data collection, with Amazon DynamoDB running on top as their database solution.
Wind Power
- High-performance ingestion of large-scale data from 15,000 turbines
- Reduced storage costs with raw data compressed to 10% of its original size
- Low query latency even with wide tables of 700 or more columns
Mingyang is a global leader in wind energy that develops large-capacity, low-wind-speed onshore and offshore wind turbines. Each of Mingyang’s 15,000 turbines has hundreds to thousands of data collection points generating hundreds of millions of data points every day. These data points are stored and used in essential applications such as centralized monitoring and analytics.
Energy Storage
- Fully managed cloud service that frees team from administration tasks
- Efficient ingestion of millions of data points every second
- Private links to TDengine Cloud for low latency and network costs
Gotion is a leading global solutions provider that focuses on researching, producing, and marketing lithium-ion batteries and electricity storage. One of their energy storage projects involved monitoring the safety of their batteries — battery charging and usage data, current, voltage, and similar metrics — in real time.
