Abstract
Wind data are vital for the research in renewable energy research. Their quality from numerical weather prediction significantly influences the wind energy models. This paper utilizes a comprehensive statistical analysis for analyzing predictive errors, named residuals of wind speed and direction modeled by numerical weather prediction models. The analysis, taken an Arctic wind site as an example, effectively integrates statistical inference, probabilistic modeling, and hypothesis tests. It is proven that the residuals still contain important meteorological information. The introduced statistical analysis may be used to replenish residuals and explore complex intrinsic properties of numerical weather wind models and contributions to wind energy modeling.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
