I've included for you the full report in AP1.pdf file.

Wind and solar power are notoriously uncertain, leading utilities to default to fossil fuels, which are available on demand. Predicting wind energy yields can reduce some of that uncertainty allowing utilities to benefit from renewable sources’ advantages and easing dependence on fossil-fuel and nuclear sources. Making accurate predictions requires a solid understanding of wind characteristics and its intermittent behavior. This report provides an exploratory data analysis for wind speed and output power time series of ten wind turbines selected from a wind farm in the US. Different visualizations are deployed for wind velocity, output power and the increments of measurements. In addition, an EOF analysis is done on the sample dataset to find the temporal bases and corresponding spatial coefficients. This is a promising approach that can be useful for spatio-temporal prediction of climate and environmental data in combination with machine learning techniques.

• 1. Introduction
• 2. Main Topic
  • 2.1. Data Description
  • 2.2. Data Visualisation
    • 2.2.1. Time Series Plots
    • 2.2.2. Heat Map
  • 2.3. Analysis
    • 2.3.1. Histograms
    • 2.3.2. Increment Analysis
    • 2.3.3. Scatter Plots and Correlation Matrix
    • 2.3.4. EOF Analysis
• 3. Conclusion and Outlook
• 4. References