The growing demand for renewable energy and the increased installation of wind turbines have brought challenges related to operational efficiency and predictive accuracy. In this thesis, we extend the method 'Narrowest Significance Pursuit' (NSP) to non-linear frameworks and explore the application of Non-Linear NSP to detect multiple change points in wind energy production data. By identifying structural shifts and anomalies, NSP can significantly enhance predictive models used in wind power forecasting. The study introduces two parametric as well as isotonic regression and S-shaped non-parametric models to handle non-linearity in wind turbine data.
The results demonstrate superior performance of isotonic regression over parametric approaches, especially in terms of detecting gradual changes and subtle anomalies. However, issues like model misspecification and computational inefficiency remain, prompting further optimization efforts. We also recommend expanding NSP methods to other renewable energy sectors, such as solar energy, to broaden the applicability of these models.

In this report a generalized Polya urn model is considered where there is nonlinear reinforcement and each component has a tness. The transition probability is proportional to feedback functions of the model and thus depends on the component that is considered. Models with these feedback functions can reach a monopoly or dierent stable state after a nite amount of time. The kind of stable state that is reached depends on the value of the non-linear reinforcement and the exact form of the stable state depends on the tness and initial conditions of the components. In this report the behavior of the model is illustrated using ow elds in the cases of k = 2 and k = 3. It is also proven when monopoly is reached for a particular feedback function and that the simplex for this function is partitioned into attractive domains for the corner points, which are convex polytopes.
cases of k = 2 and k = 3. It is also proven when monopoly is reached for a
particular feedback function and that the simplex for this function is partitioned
into attractive domains for the corner points, which are convex polytopes.