A significant decrease in greenhouse gas emissions can be achieved by including a prediction of future power consumption in the control of ships’ power plants during transits. Moreover, including a prediction of power consumption in the Energy Management System (EMS) during Dynamic Positioning (DP) operations can also contribute to a reduction in greenhouse gas emissions. However, predictions of future power consumption for DP operations, as well as its implementation in an EMS, have not been investigated widely, since this is a complex task for a relatively specific application. This research aims to investigate how power consumption during DP operations can most accurately be predicted for near-future and far-future, within reduced computational requirements for real-time applications.

In this research, six different approaches for power consumption prediction are investigated. These approaches can be categorized into projection models, which determine the power consumption independent of time based on the environmental conditions, and forecast models, which rely on the recent past behaviour of the system.
The four projection models consist of two Physical Models (PMs), a Data-Driven Model (DDM), and a hybrid model. The first PM is the static model, in which the environmental loads are directly compensated by the thrusters, hence the ship is assumed not to move. The second PM is the dynamic model, in which the motions of the ship are also modelled. Then, the DDM projection is based on Kernel Regularized Least Squares (KRLS) to capture the nonlinear relation between environmental conditions and power consumption based on historical data. Lastly, the hybrid projection model is an integration of the dynamic model in the DDM.
Afterwards, two forecast models are developed, being one DDM and one hybrid model. The DDM forecast is based on a combination between KRLS and Time Series (TS) forecasting. The hybrid forecast is an integration of the dynamic model in the DDM forecast.

Simulations are performed for each model using a data set provided by RH Marine. The DDMs show the most accurate results, taking into account the required computational effort. The results show that the forecast model, using the combination between KRLS and TS, predicts the near-future at 1 s in the future with 2.8 % error, increasing to 6.9 % at 120 s in the future. Afterwards, the projection model, using merely KRLS, predicts the far-future up to the length of the operation horizon with an accuracy of 11.2 %, based on the weather forecast. This multi-horizon prediction of power consumption can enable the EMS to make accurate short-term decisions in the control of the power plant and to define optimal scheduling of the power plant components over the
complete horizon of the DP operation.