Wind Speed Correlation.pdf

11322ijit01.pdf
Preview of Wind Speed Correlation
🔗 Source: aircconline.com
📊 Size: 504 KB
👤 Author: Amanda Goodrick
⬇️ Downloads: 181

Summary

Key Findings:

Mutual information shared between temperature and wind speed was greater in Lincoln, Nebraska than in Boston, Massachusetts.
Linear correlation coefficients were weak in both cities, verifying the need for a different method to investigate the relationship.
Kernel density estimation (KDE) provided a closer fit to the data than normal distribution curves.
Mutual information indicated 0.4 bits of information shared between the two variables in Boston with the KDE.

Methodology:

Hourly temperature and wind speed data were retrieved for two weeks for Boston, MA and Lincoln, NE.
Differential entropy was used to calculate mutual information between the hourly change in each variable.
A smoothing method estimated the actual data by fitting a smooth curve to the histogram of the data.
Kernel smoothing distribution provided an improved data fit.

Results:

Descriptive statistics and linear correlation coefficients are presented in Table 1.
Scatterplots and histograms are presented in Figures 2-5.
Entropies and mutual information are presented in Tables 2 and 3.
Mutual information indicated a stronger correlation between temperature and wind speed in Lincoln than in Boston.

Description

Temperature and wind speed correlation is analyzed using information entropy. Hourly data from Boston and Lincoln is compared, revealing a stronger correlation in Lincoln. Mutual information is calculated using differential entropy.

Technical Information

  • File Format: PDF
  • File Size: 504 KB
  • Pages: 6
  • Language: EN
  • Author: Amanda Goodrick
  • Total Downloads: 181
  • Last Updated: 3 weeks ago

Document Overview

This PDF document about Wind Speed Correlation provides comprehensive information and guidance. Whether you're a beginner or advanced user, this resource offers valuable insights into Wind Speed Correlation.

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