Services Catalogue

The Cascaded Hydropower Forecasting service provides integrated short-term forecasting for the Aliakmonas River hydropower cascade in Northern Greece. Track 1 predicts natural inflows to Ilarionas reservoir; Track 2 predicts electrical production at Asomata HPP, addressing the unique challenge of forecasting in an incomplete cascade where operational data from the intermediate Polyfyto reservoir is unavailable. Both tracks operate at daily resolution over a 1-7 day horizon.

The PV Production Forecasting service delivers short-term and day-ahead photovoltaic power production forecasts for PPC's portfolio of PV generation sites. Using 15-minute multi-sensor site telemetry — including inverter measurements, irradiance, soiling sensors, and solar geometry features — the service provides uncertainty-quantified production estimates at site and inverter level, supporting imbalance management, grid constraint anticipation, and operational planning.

The Wind Farm Event Detection service delivers automated identification, classification, and energy-loss quantification of operational events in wind turbine SCADA streams. Events are categorised into four classes: Environmental, Manufacturer, Utility, and Grid. The service enables systematic comparison of event patterns across turbines and seasons, shortens detection-to-awareness latency, and feeds availability dashboards and energy loss accounting workflows.

The Pumped-Storage Optimisation service provides intelligent operational scheduling for the Sfikia-Asomata coupled hydropower system, optimising bidirectional Sfikia pumped-storage operations to maximise system-wide economic value under varying electricity market prices and water availability conditions. The service operates in two modes: day-ahead scheduling (24h daily) and real-time adjustment via rolling 48h Model Predictive Control updated every 6 hours.

The Wind Power Short-Term Forecasting service delivers probabilistic day-ahead and intra-day wind power forecasts at turbine and farm level using LightGBM ensembles trained on multi-model NWP inputs and SCADA telemetry. The service addresses three interconnected challenges: NWP model selection and weighting, non-linear power curve mapping, and SCADA integration for curtailment and maintenance state accounting. Forecasts are generated in day-ahead (13-40h), extended (40-84h), and intraday rolling (0-12h) modes.

The Sustainable Supply Chain Optimiser generates optimised supply chain plans for multi-stage networks by simultaneously minimising cost, carbon emissions, and lead times while satisfying physical constraints. The service integrates sustainability metrics — including energy consumption and CO2 emissions per supplier and transportation mode — alongside traditional cost and service level objectives, helping organisations meet sustainability targets without compromising operational efficiency.

The AI Manufacturing Process Digital Twin creates virtual representations of physical manufacturing processes using real-time sensor data fusion and continuously updated AI models. The service enables real-time monitoring of process state, simulation of parameter changes, and generation of optimisation recommendations — allowing operators to prevent defects, reduce energy waste proactively, and improve process quality through continuous what-if analysis.

The Energy-Efficient Process Planning Optimiser generates energy-efficient manufacturing process plans that optimise the selection of manufacturing processes, their sequences, machine assignments, and process parameter values. By simultaneously minimising energy consumption, cycle time, and manufacturing cost while ensuring quality compliance, the service embeds energy efficiency into the planning phase of production before manufacturing begins.

The AI-Assisted Production Scheduling service generates optimised production schedules that allocate and sequence manufacturing tasks across available resources, with energy efficiency as a primary optimisation criterion alongside makespan and due-date compliance. By evaluating alternative machine sequences and task assignments using AI and combinatorial optimisation, the service identifies schedules that minimise total energy consumption for equivalent production output.

The AI-Driven Energy Management service optimises power allocation between fuel cells and batteries in Fuel Cell Hybrid Electric Vehicles (FCHEVs) using a reinforcement learning framework. The service minimises hydrogen consumption, maintains battery SoC, and extends powertrain component lifetime through adaptive power-split decisions evaluated over complete driving cycles.