A comprehensive statistical analysis for residuals of wind speed and direction from numerical weather prediction for wind energy

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 mo...

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Bibliographic Details
Published in:Energy Reports
Main Author: Hao Chen
Format: Article in Journal/Newspaper
Language:English
Published: Elsevier 2022
Subjects:
Numerical weather prediction
Residual analysis
Statistical modeling
Hypothesis test
Wind energy
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
Arctic
Online Access:https://doi.org/10.1016/j.egyr.2022.07.080
https://doaj.org/article/1377632a7034458b9b4e7547dc821264
Description
Summary: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.

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