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Estimation of Uncertainty of Wind Energy Predictions

With Application to Weather Routing and Wind Power Generation


David Zastrau

Currently, a new generation of fuel-efficient ships, which use wind force in addition to conventional propulsion technology, is being developed. This study describes a mathematical method for a probabilistic estimate of the wind propulsion force on a ship route. The method is based on quantile regression, which makes it suitable for various ship routes with variable weather conditions. Furthermore, the author takes different macro weather situations into account for the calculation of the statistical distributions. He validates the results for a multi-purpose carrier, a ship route in the North Atlantic Ocean and archived weather forecasts. It showed that the wind force can be estimated more accurately if the macro weather situation is taken into account properly.

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4. Estimation of Prediction Uncertainty


Abstract: This chapter describes the state of the art of uncertainty estimation for weather predictions and weather-based energy predictions. First ensemble prediction systems (EPS) are discussed which estimate uncertainty in weather predictions by the spread of a set of possible weather scenarios. Then non-parametric statistical methods are discussed as alternatives to EPS. Quantile regression is selected as method for uncertainty estimation in this work since it is robust, fast and an established method in the field of uncertainty estimation of wind energy predictions.


4.1. Theoretical and empirical models

4.2. Ensemble prediction systems

4.2.1. Multi-model and multi-analyses ensembles

4.2.2. Super ensembles

4.3. Statistical methods

4.3.1. Probability density estimation methods

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