Problem
Transmission (TSO) and distribution (DSO) system operators across Europe are facing a new challenge: integrating fluctuating renewable energy sources (RES) into the grid. Unstable production from wind and solar farms, combined with the lack of accurate forecasts and rising consumer demand, leads to infrastructure overloads, energy waste, and—in extreme cases—blackouts.
Goal
Leverage artificial intelligence to forecast energy demand, optimize power flow in the grid, and detect anomalies early—improving stability, reducing operational costs, and maximizing RES utilization.
Story
A DSO in Central Europe was experiencing difficulties in balancing energy flows from connected solar farms. Without accurate consumption forecasts and with highly variable PV production, the operator faced inefficient grid use, expensive reserve activations, and increased risk of overloads.
To solve this, the operator implemented an AI-based system that analyzed weather forecasts, IoT data (smart meters, inverters, SCADA), and historical energy usage. Machine learning algorithms provided near real-time demand forecasting (with a 15-minute horizon) and enabled dynamic load balancing across the grid. The system also monitored deviations in voltage and frequency to detect early signs of failure or stress.
Within six months, the operator reduced the need for expensive reserve capacity by 30%, and overload-related incidents dropped by 40%. Operational savings and improved network stability led to a full return on investment (ROI) in just 24 months.
Results (KPI)
Thanks to the AI deployment, the grid operator:
✅ Improved demand forecast accuracy to 93%
✅ Reduced reserve energy usage by 30%
✅ Cut overload and failure events by 40%
✅ Shortened anomaly response time from hours to minutes
✅ Achieved full ROI in 24 months
Project summary
Selection of an AI platform to process data from SCADA, IoT, and ERP systems
Integration with network monitoring tools and weather APIs
Training ML models on historical data, then enabling real-time forecasting
Automated anomaly detection and live optimization of power flows
KPI reporting and integration with dashboards for technical teams
Scope of implementation
- Demand forecasting: AI analyzing weather, usage history, and consumer behavior
- Real-time optimization: Dynamic grid control using AI models
- Fault detection: Algorithms detecting voltage, frequency, and load anomalies
- Integration with SCADA/ERP: Data visibility and automated operational decisions
Technology stack – core and supporting
⚙️ Artificial Intelligence (AI): Predictive machine learning models used for demand forecasting, load optimization, and anomaly detection
⚙️ Internet of Things (IoT): Data from smart meters, inverters, and grid-level sensors
⚙️ Big Data: Real-time processing of time series and telemetry data
⚙️ Predictive Analytics: Algorithms identifying patterns and trends in energy consumption and grid performance
⚙️ SCADA/ERP Integration: AI embedded into existing operational infrastructure
