Machine learning and AI built for energy trading — not adapted from somewhere else.
Our data science team focuses on advanced analytics and predictive modelling in energy trading. We build custom models, train them on real market data, and integrate them into live trading workflows.
ML models that predict actual power load profiles for intraday trading. Incremental training keeps accuracy as market conditions shift. Our work has delivered 37% imbalance reduction for one client.
Building and maintaining forward curves for power, gas, and other commodities. Fundamental analysis integrated with quantitative methods.
ML-based production forecasts for individual parks and portfolios. 18-month horizon models that capture park-specific shutdown behaviour.
Real-time news analysis for market impact: categorise, summarise, and estimate price impact from live news feeds. Delivered via MS Teams integration.
Battery Energy Storage System optimisation models for revenue maximisation and grid stability.
Our client supplies electricity to a large German industrial facility whose load swings between a few MW and over 100 MW within quarter-hours. Without correction, imbalance reached 33% of actual consumption. Their existing Random Forest model cut that to 26%. We replaced it with a Temporal Fusion Transformer trained on 96 past quarter-hours, day-ahead nominations, and shift schedules — predicting 30 minutes before delivery. We also designed a market-neutral correction strategy so buying and selling cancel out at the margins, making the operation zero-cost regardless of imbalance price dynamics.
