METIS adds electric profiling functionality to vessel performance analytics

METIS adds electric profiling functionality to vessel performance analytics

METIS Cyberspace Technology has launched an Electrical Power Profile evaluation application to its vessel performance analytics tools.

The new METIS Electrical Power Profile Overview functionality responds to growing market interest in the benefits of installing alternative electrical power sources to auxiliary engines as a cost-effective and sustainable way of covering energy needs during port operations.

With additional auxiliary power being useful for tasks as diverse as manoeuvring, acceleration, anchoring, loading and discharge, the METIS Electrical Power Profile application offers a data-based evaluation for feasibility studies covering alternative electrical power sources. Calculations are based on data acquired automatically by vessel sensors, without any human intervention, with even the detection of the ship’s operational status drawing on an advanced algorithm that is part of the METIS AI platform.

“A statistical analysis can be generated based on electrical energy consumption distribution during each operational activity,” said Serafeim Katsikas, chief technical officer at METIS. “With this kind of analysis, a user could evaluate – for example – whether its operations within the port or ECA zone would be made more cost effective by installing a power pack.”

The METIS Electrical Power Profile app can measure, report and visualise energy and power consumption across any operational status in the user’s preferred format, offering shipowners greater insights into vessel management excellence. It can also take account of all data gathered subsequent to the installation of the METIS data analytics solution, Katsikas added.

The new functionality builds on the company’s Fuel Oil Consumption Profile Overview, which harvests data available to METIS analytics to quantify all factors affecting a vessel’s fuel consumption based on three months performance and machine learning models.