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Supervised Machine Learning in Wind Forecasting and Ramp Event Prediction provides an up-to- date overview on the broad area of wind generation and forecasting, with a focus on the role and need of Machine Learning in this emerging field of knowledge. Various regression models and signal decomposition techniques are presented and analyzed, including least-square, twin support and random forest regression, all with supervised Machine Learning. The specific topics of ramp event prediction and wake interactions are addressed in this book, along with forecasted performance.

Wind speed forecasting has become an essential component to ensure power system security, reliability and safe operation, making this reference useful for all researchers and professionals researching renewable energy, wind energy forecasting and generation.

Degradation, Mitigation and Forecasting Approaches in Thin Film Photovoltaics covers detailed descriptions of cell to module level fundamentals of photovoltaics including thin-film materials, performance, maintenance procedures and performance forecasting approaches.

Designed to help readers better understand the complexities of photovoltaics, covering the most important aspects of PV cell design, fabrication and performance limiting issues coupled with case studies dedicated towards the forecasting approaches for performance degradations occurring in PV panels. Through theoretical and experimental techniques and methods for performance prediction of the PV cells and modules, this reference concludes with an analysis of the emerging PV technologies for the future.

Particularly helpful to researchers because the chapters are aligned in a way that enables readers to start from the fundamentals of PVs and end up with a sound understanding of the current and upcoming PV challenges and ways to deal with them.