Built for Generation-Side Data Management
Unified data ingestion from centralized and distributed power sources and storage assets — enabling operational monitoring, early warning, optimization, and intelligent dispatch.
Request DemoAs the generation-side landscape grows increasingly diverse — spanning thermal, wind, hydro, nuclear, and biomass, alongside virtual power plants that aggregate distributed generation, controllable loads, and energy storage — assets are instrumented with thousands of measurement points, with DCS, SCADA systems, and edge gateways continuously collecting operational, environmental, and equipment status data at millisecond-to-second intervals.
Grid operators, production teams, O&M staff, and virtual power plant operators rely on this time-series data for real-time monitoring, health diagnostics, output and clearing forecasts, and energy efficiency analysis.

DolphinDB serves as the time-series database and compute engine for the generation side — ingesting data from DCS/SCADA and edge collection systems, organizing high-frequency measurement data by unit, type, and equipment dimensions, and retaining years of historical data online to support downstream analysis and decision-making.
Leveraging built-in time-series analytics, windowing operations, event detection, and stream processing, DolphinDB identifies early fault indicators, triggers rule-based alerts, performs comprehensive thermal parameter analysis, and runs power output and load forecasting models in real time.
Stream and historical data are managed within a single platform, with identical metric definitions and computation logic across real-time and batch workflows, providing continuous, reliable data and compute support for dispatch decisions and energy efficiency optimization.
Via APIs and plugins, DolphinDB exposes unified data and analytics as services, enabling production management, O&M, dispatch systems, and visualization dashboards to operate in sync and make well-informed decisions.
High-efficiency compression and columnar computation for high-frequency time-series data dramatically reduce storage costs while enabling complex queries and aggregations in seconds.
Storage, real-time computing, historical analysis, and model execution are consolidated into a single platform — reducing data movement between systems, minimizing component coupling, and improving operational efficiency.
Fast cross-unit, multi-dimensional comparison and correlation analysis across extended time ranges provides a solid data foundation for unit optimization, energy efficiency improvement, and operational decision support.