With increasing restrictions on ship carbon emissions

it has become a trend for ships to use zero-carbon energy such as solar to replace traditional fossil energy. However

uncertainties of solar energy and load affect safe and stable operation of the ship microgrid. In order to deal with uncertainties and real-time requirements and promote application of ship zero-carbon energy

we propose a real-time energy management strategy based on data-driven stochastic model predictive control. First

we establish a ship photovoltaic and load scenario set considering time-sequential correlation of prediction error through three steps. Three steps include probability prediction

equal probability inverse transformation scenario set generation

and simultaneous backward method scenario set reduction. Second

combined with scenario prediction information and rolling optimization feedback correction

we propose a stochastic model predictive control energy management strategy. In each scenario

the proposed strategy has the lowest expected operational cost of control output. Then

we train the random forest machine learning regression algorithm to carry out multivariable regression on samples generated by running the stochastic model predictive control. Finally

a low-carbon ship microgrid with photovoltaic is simulated. Simulation results demonstrate the proposed strategy can achieve both real-time application of the strategy

as well as operational cost and carbon emission optimization performance close to stochastic model predictive control.