Services Catalogue

The Heating Consumption Optimisation service analyses thermal energy consumption within the Torrelago district heating network to identify inefficiencies, detect anomalous consumption patterns (including elevated return temperatures and abnormal demand during mild conditions), and optimise heat distribution across connected buildings. Using statistical analysis, contextual peak detection, and AI models (LSTM, XGBoost), the service provides actionable insights for energy efficiency improvements.

The Building Flexibility & Demand Response Optimisation service uses AI to actively manage electricity consumption across a municipal building portfolio in response to external grid requests, dynamic tariffs, and cost signals. By analysing incoming consumption data alongside grid operator notifications and tariff conditions, it generates actionable curtailment and rescheduling recommendations that building operators can review and approve before implementation. The result is a systematic approach to demand response participation that reduces peak-hour expenditure and positions the municipal estate as a controllable flexibility asset.

The AI-State Estimation service delivers high-resolution near-real-time representations of the operational state of LV/MV distribution grids. By combining AI-generated load profiles for unmetered assets with measured feeder-level SCADA data and a full power-flow solver, the service computes voltage magnitudes, angles, active/reactive power flows, and currents for every node and branch including those without direct measurement.

The AI-based State Estimation Forecast service delivers 0-48 hour predictions of the full electrical state of a distribution grid, combining AI load/generation forecasting with a physical power-flow solver to produce future values of active/reactive power flows, voltage magnitudes, and asset utilisation for every network component. The service enables DSOs to anticipate congestion, thermal overloads, or voltage violations before they occur.

The AI-Enhanced Model-Based Ancillary Services service implements a hybrid approach combining physics-based simulation (PowerFactory) with data-driven analysis to identify systematic deviations between the network model and real-world measurements. By learning recurring deviation patterns — at specific times of day, load profiles, or seasonal conditions — the service applies a correction layer on top of the physical model to improve reliability of model-based grid management processes.