Accurate Wind Resource Assessment (WRA) requires the correction of systematic errors in modeled wind fields using sparse and temporally limited on-site measurements. With the emergence of microscale Large-Eddy Simulation (LES) as a high-resolution alternative to traditional mesoscale models, current correction practices, such as Measure-Correlate-Predict (MCP) for temporal extrapolation and Inverse Distance Weighting (IDW) for spatial extrapolation, need to be re-evaluated and adapted to the microscale context. This thesis investigates how data-driven methods can improve LES wind-speed error correction in both time and space, addressing three research gaps: the lack of validation of MCP on microscale data, the absence of temporal and spatial context in standard MCP-style formulations, and the limited use of flow information in spatial extrapolation.

The temporal component evaluates a set of linear and nonlinear regression models as flexible MCP-style baselines and extends them with additional contextual information. Temporal structure is incorporated through multi-step time windows, and local spatial structure is added by including neighboring LES grid cells. To represent full-field atmospheric patterns, the models are further enriched with latent encodings of the LES wind-speed field obtained through a Convolutional Autoencoder. The results show that MCP generalizes well to the microscale and that adding temporal and spatial context improves time-series accuracy across all observation locations, with combined strategies outperforming industry-standard methods.

The spatial component introduces Wind Speed-enhanced IDW (WS-IDW), which augments traditional IDW by weighting observation locations not only by geographic distance but also by similarity in LES wind speed. WS-IDW produces consistent improvements over the baseline, particularly when more observation locations are available. Analysis of the correction maps reveals that WS-IDW partially smooths misplaced fine-scale streaks in the LES wind field, supporting the hypothesis that LES is prone to slight spatial misalignment of coherent structures. The proposed method generalizes reasonably across sites and across different numbers of source masts.

Together, the temporal and spatial results demonstrate that incorporating LES-derived spatial and temporal information yields systematic improvements in microscale correction performance. The thesis provides a refined understanding of how LES behavior interacts with data-driven correction methods and offers a foundation for developing more robust microscale WRA correction frameworks in future work.

This paper presents a comparative study of multiple algorithms that can be used to automatically search for high-performing pipelines on machine learning problems. These algorithms, namely Very Large-Scale Neighbourhood search (VLSN), Breadth-first search, Metropolis-Hastings, Monte-Carlo tree search (MCTS), enumerative A* search, and Genetic Programming, are evaluated on three datasets. The performance of VLSN is consistently acceptable for this task, but the best performance is given by MCTS. Interestingly the results show that limiting the solution space to pipelines containing only a classifier operator does not significantly decrease performance. Three possible explanations for this are that the datasets used are too simple, the use of default hyperparameters makes preprocessing and feature selection operators useless, or the evaluation of pipelines on a limited training set makes the search procedure ineffective. This research contributes to the field of AutoML by shedding light on algorithm performance and providing insights for future improvements.