Meteorology IoT Data Platform

Protecting power lines from extreme weather with a real-time IoT data platform

Together with a partner, MADIS delivered a platform that turns data from weather stations, line sensors and sky cameras into early warnings and machine learning forecasts.

At a glance

Real-time

processing of IoT weather data streams

3 source types

weather stations, line sensors, sky cameras

Hot + cold

streaming and batch analytics in one platform

Summary: Wind gusts, storms and icing can threaten the stability of the power transmission network. With one of our partners, MADIS built an end-to-end IoT data platform for the client that collects real-time weather data, issues timely alerts and uses machine learning to forecast weather-related risks – enabling proactive maintenance of transmission lines.

The client

The client operates electricity networks where a steady flow of energy and a transparent view of the grid are essential. Weather is one of the biggest external factors affecting how the network performs.

The challenge

Keeping energy transmission stable means anticipating what the weather will do to the lines, not just reacting to it. Extreme and changing conditions such as strong wind, storms or ice build-up on the lines can create risks for the network.

Data on these conditions comes from many devices in the field, which all have to be integrated reliably and in real time.

• Many devices, many formats: meteorological stations, transmission line sensors and sky cameras all send data.
• Time matters: warnings are only useful if they arrive before conditions turn critical.
• Reliable data: decisions about the grid can only be based on validated measurements.

Our approach

We designed the platform as one end-to-end chain, from the sensor in the field to the alert on the operator’s screen.

Data acquisition and quality

Robust integration and management of the IoT devices ensures reliable data collection, while rigorous validation and continuous monitoring of the measurements keep data quality under control.

Processing and alerting

A dual-mode architecture handles both streaming (hot) and batch (cold) analytics. A flexible, rule-based alarm system issues critical warnings, for example for wind gusts, storms and icing conditions.

Machine learning forecasting

An MLOps framework runs predictive models for short-term weather forecasting, cloud cover estimation and power line icing prediction.

Visualisation and integration

A reporting layer with geospatial maps and self-service tools makes the results easy to use, and external interfaces let other systems use the data for real-time intervention and control.

The solution

The delivered solution is a scalable data lakehouse platform on Azure that collects, validates, integrates and processes real-time data streams from all connected IoT sources. It produces indicators and aggregates, generates timely and actionable alerts and feeds machine learning forecasts back to the people managing the network.

Technologies: Azure IoT Hub · Event Hub · Event Grid · Databricks · PySpark Structured Streaming · Azure Data Explorer · Azure Data Lake · Azure Data Factory · Azure Machine Learning · Power BI · .NET · Python · GitLab

Results

• Proactive maintenance: transmission lines can be maintained proactively, even under extreme and variable weather conditions.
• Early warnings: weather-related risks to the transmission network can be anticipated and prevented.
• Broader value: the platform supports network management, network security and energy trading decisions.

Facing a similar challenge?

Want to turn sensor data into real-time decisions? Talk to our data team about IoT and streaming platforms.