Work

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

Year: 2022

Type: article

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.

Source: Energy Reports

DOI: 10.1016/j.egyr.2022.07.080

Author: Hao Chen

Institution: United Nations Conference on Trade and Development

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDG: Climate action

Open Access status: gold

Estimated APC paid: $2,240