The global energy consumption relies heavily on fossil fuels, which are projected to be depleted by 2050. This makes it necessary to optimize renewable sources such as wind energy. However, wind energy faces challenges due to the unpredictability and turbulence of wind patterns. Traditional wind turbines are limited by their reactive approach to turbulence. To address this issue, the integration of lidar technology in wind turbines provides a predictive advantage, enhancing the control of wind parks by offering insights into incoming wind
disturbances and improving feedback mechanisms. A new method for adjusting ground-based lidar turbulence intensity measurements is investigated in this master’s thesis, aiming to reduce measurement uncertainties. A novel turbulence intensity equation, developed using perturbation theory, serves as the foundation for an adjustment model that has been validated against existing standards. This adjustment method was tested in both virtual environments and on an actual wind site in the Netherlands, demonstrating its effectiveness in reducing uncertainties associated with lidar data. The study also utilizes the findings of a joint-industry project to refine data precision and compares various adjustment methods, ultimately showing that the perturbation-based adjustment improves the alignment of lidar measurements with traditional met mast data. Furthermore, the thesis explores the acceptance of lidar technology through a model based on the technology acceptance
framework, revealing that reducing measurement uncertainties positively impacts the perceived usefulness and adoption of lidar systems. This research aims to enhance the accuracy and reliability of wind energy assessments using ground-based lidar, paving the way for broader adoption in the industry. Future work should focus on expanding the dataset, incorporating advanced machine learning techniques, and extending the turbulence
intensity equation to accommodate complex terrains, further reducing uncertainties in lidar measurements.