Effect of weather forecast uncertainty on offshore wind farm availability assessment

With the growing demand for offshore wind energy and the continued drive for reduced levelised cost of energy, it is necessary to make operation and maintenance activities more effective and reduce related costs. A key factor in achieving this aim is to more representatively model operation and maintenance activities, and to do this, simulation models should include more accurate weather forecasting algorithms. In this paper, three weather forecast modelling methods are used to generate projections of wind and wave values which are then used as inputs in an operation and maintenance simulation model. These methods include Markov Chains, gradient boosting and a novel hybrid regression/statistical approach which has been developed and is presented herein. The change in key performance indicators after the wind farm lifespan is simulated using the forecasting methods and then compared to one another. It is shown that the Markov Chain and hybrid models numerically perform similarly, although the hybrid method has some additional desirable features. Finally, it is shown that the effect of this type of modelling uncertainty leads to significantly differing performance estimates through the operation and maintenance model.