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

Solar Power

TDengine enables solar farm applications like performance monitoring, energy forecasting, and solar tracker optimization.

Wind Power

TDengine enables wind farm applications like operational optimization, predictive maintenance, and asset management.

Energy Storage

TDengine enables BESS applications like EMS optimization, grid stability, and energy arbitrage.

Data Challenges for Renewable Energy Operators

01
Data Volume & Scale

Sites generate enormous amounts of time-series data from numerous sensors and monitoring devices.

02
Storage Costs

The massive datasets that power next-generation applications take up huge amounts of disk space.

03
Disparate Platforms

Different sites may run on different systems and protocols, making global analytics an arduous task.

04
Analytics Latency

Applications such as condition monitoring need real-time analysis of streaming data from sensors.

How TDengine Can Help

01
High Performance

Ingest and store petabytes of data from billions of sensors with no performance deterioration.

02
Efficient Storage

Compress your datasets to 1/10 of their original sizes and reduce overhead with tiered storage.

03
Cross-Site Consolidation

Centralize data from all sites and platforms in our unified system through zero-code connectors.

04
Real-Time Processing

TDengine supports real-time data processing, allowing for immediate analysis and decision-making.


What Our Customers Say

Solar Power

Why TDengine?
  • 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
Background

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.

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Wind Power

Why TDengine?
  • 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
Background

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.

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Energy Storage

Why TDengine?
  • 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
Background

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.

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Learn how TDengine can help you achieve your data goals and implement modern applications in your sites

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