Expected Impact
Indicators & Values
Work Stream 1 – Energy Management

Detailed knowledge of energy flows and consumers behaviour inside the railway system

The IN2DREMS smart metering platform will allow real time measurements of energy flows within the entire railway system. Based on these measurements, IN2DREAMS advanced analytics platform will recommend the optimal energy efficiency strategies including optimal driving profiles, time tables, optimal journey and ticketing policies for the passengers

Energy profile prevision improvement. Refined knowledge of the traffic disturbances and consequences on system’s energy profiles.

IN2DREAMS will develop energy forecasting algorithms, able to predict the load portfolio according to different horizons and different accuracy. These algorithms will set the baseline for proactive decision making in future railway systems, lowering the risk of unwanted events, decreasing the energy consumption and contributing to the reduction of costs

Improved reliability and LCC

This will be achieved through continuous monitoring of parameters such as voltage, energy, current etc. and the adoption of advanced signal processing techniques for fault detection

Optimized ROI and ability for developing a better business plan

The development of new business models that allow highly dynamic pricing and power auctioning will further help to shift the peak loads, by providing appropriate incentives to the end users according to the load conditions of the system and the available energy sources
Work Stream 2 – Asset Management

Improvement of capacity – a large improvement in line capacity due to a more effective asset maintenance management

The work done within WS2 will pave the way towards future Intelligent Asset Maintenance, aiming at supporting the deployment, usage and maintenance of railway assets in a more effective and cost-efficient way. Data-driven methodologies will enable the analysis of railway asset data at system-level, providing a holistic approach to asset management and decision support including diagnostics and prognostics

Improved Reliability: failure modes of current systems

will be reduced/eliminated due to the new “intelligent asset management”

The application of risk assessment through developed algorithm metrics and failure prognosis within decision-making will help to reduce unexpected maintenance interventions, thus increasing reliability and availability

Significant LCC savings

The work done within WS2 will allow moving from reactive and preventive maintenance to prescriptive maintenance based on nowcasting and forecasting of asset condition and diagnosis

Improved safety

As a consequence of improved reliability, the number and magnitude of incidents will be reduced


This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No: 777596

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