We apply large-scale data infrastructure and machine learning to the most complex signals in energy — smart meters, PMUs, SCADA telemetry, and grid-edge IoT — transforming raw measurement into operational insight for a cleaner, more reliable electric future.
Structured summaries of academic research and industry literature on big data frameworks — Hadoop, Kafka, Flink, Spark Streaming — applied to power system operations.
Comparative studies of data pipeline architectures for load forecasting, fault detection, and predictive maintenance of transformers and transmission assets.
Reference code and pseudocode for smart meter preprocessing, feature engineering, and machine learning pipelines — from consumption clustering to anomaly detection.
Modern smart grids generate unprecedented volumes of heterogeneous data: synchrophasors at 30–60 samples per second, AMI readings every fifteen minutes, SCADA telemetry, weather overlays, and streaming feedback from distributed energy resources across millions of endpoints.
Rongheng Grid Analytics focuses on the infrastructure and methods that make this data usable — not as a dashboard, but as operational intelligence feeding forecasts, fault diagnostics, and long-horizon planning for a decarbonizing power system.
Our work sits at the intersection of distributed systems engineering, time-series analytics, and applied machine learning, informed by a deep respect for the physics and economics of the electricity grid.
Training forecast and detection models across data silos without exposing raw measurements between operators.
Low-latency inference on substation and feeder hardware for protection, islanding, and local control.
Co-simulation of physical grid behavior with stochastic generation to stress-test operating envelopes.