Services Catalogue

The AI-Driven Active Control service provides an adaptive control framework for hydrogen technologies within renewable energy microgrids, optimising the coordination between electrolysers, fuel cells, and energy storage systems to ensure efficient energy flow between renewable generation, hydrogen production, storage, and consumption. The service aims to maximise renewable utilisation and improve microgrid flexibility and resilience.

The Data-Driven Predictive Modelling service provides an AI-based framework for predicting the performance evolution and degradation of hydrogen fuel cell systems using time-series operational data. Based on LSTM networks with self-attention mechanisms, the service supports condition monitoring, predictive maintenance, and lifecycle optimisation by delivering reliable forecasts of stack voltage as the primary performance health indicator.

The AI-Enhanced Multi-Agent Testing for V2G Applications service provides a simulation and validation environment for evaluating decentralised V2G control strategies based on AI-driven multi-agent systems. The service models EV fleets interacting with energy markets, grid signals, and operational constraints, enabling pre-deployment validation of decentralised coordination strategies including stability analysis, fairness assessment, and robustness testing.

The Battery & EV EMS Optimisation service provides coordinated optimisation of stationary battery systems and EV charging assets to maximise PV self-consumption within energy communities. Using a rolling-horizon MPC framework updated every 15 minutes, the service generates coordinated operational schedules for battery dispatch and EV charging that respect technical constraints (battery SoC bounds, EV departure requirements, charger power limits) while maximising renewable energy utilisation.

The Wind Generation Forecasting service provides high-resolution short-term and day-ahead wind power production forecasts for EV-integrated energy communities, supporting battery scheduling and — when EV telemetry becomes available — EV-aware flexibility coordination. Currently deployed for the Beckerich energy community with a 4.2 MW wind asset and 60 kW/160 kWh battery, the service is designed to be battery-ready and EV-ready.

The Localised PV Generation Forecasting service provides high-resolution photovoltaic generation forecasting for energy communities, combining data-driven machine learning with physics-informed feature engineering. The service produces deterministic and probabilistic forecasts at 15-minute resolution for short-term and day-ahead horizons, enabling community operators to anticipate renewable availability and improve scheduling of flexible loads.

The EV-Driven Demand Forecasting service predicts both aggregate and disaggregated electricity demand in residential energy communities with significant EV adoption, explicitly accounting for EV charging behaviour, household consumption patterns, and external contextual factors. The service supports operational planning, demand response strategy design, and self-consumption optimisation for DSOs, aggregators, and community managers.

The EV-User Charging and Usage Profiles Prediction service estimates when EV users are likely to charge, where charging activity is expected to occur, and how much energy is likely to be consumed during future sessions. By incorporating behavioural, spatial, and contextual factors, the service provides actionable forecasts that support infrastructure planning, congestion mitigation, smart charging strategy design, and flexible demand coordination.

The Digital Twin for DHN Optimisation provides a virtual representation of the Torrelago district heating network, enabling advanced monitoring, simulation, and scenario-based optimisation of thermal energy distribution. By integrating real-time and historical data with demand forecasting outputs, the digital twin allows evaluation of alternative operational strategies (supply temperature changes, load distribution, control setpoints) without impacting real system operation.

The Heating Demand Forecasting service uses AI-based models (LSTM and XGBoost) to forecast short-term thermal energy demand in the Torrelago district heating network. By integrating historical consumption patterns with external temperature forecast datasets, the service enables proactive decision-making and improved planning of heat production and distribution across the network.