@CONFERENCE\{IMM2013-06723,
    author       = "B. P. Pedersen and J. Larsen",
    title        = "Gaussian Process Regression for Vessel Performance Monitoring",
    year         = "2013",
    month        = "apr",
    booktitle    = "12th International Conference on Computer and {IT} Applications in the Maritime Industries ({COMPIT} 13)",
    volume       = "",
    series       = "",
    editor       = "",
    publisher    = "",
    organization = "",
    address      = "15-17 April 2013, Cortona, Italy",
    url          = "http://www2.compute.dtu.dk/pubdb/pubs/6723-full.html",
    abstract     = "It is showed how Gaussian Process Regression (GPR) can be used equally good or better than Artificial Neural Networks (ANN) for short and long term predictions of the energy consumption on a ship. Using different data sets, from five sister container ships, the method is tested with very different data quality, quantity and prediction horizon and shows that {GPR} is equally accurate and computational more efficient than {ANN,} but also offer the possible of finding the predictive variance. Furthermore, the used of so-called characteristic length-scales can be used for evaluating the importance of the input variables for the propulsion performance. A crude introduction to {GPR} is given together with how it has been applied for this purpose."
}