🌬️ Wind Power Generation Forecasting– Week 2 Report

📅 Internship Progress: Week 2

This week focused on performing Exploratory Data Analysis (EDA) to gain deeper insights into the dataset and understand the relationships between various meteorological features and wind power output.

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📊 Visualizations

To better visualize trends and correlations, the following plots were created:

1. 🔵 Scatter Plots

• Purpose: To observe the relationship between power output and features like:
  • temperature_2m
  • windspeed_100m
  • winddirection_10m, etc.
• Insight: Helped identify patterns and potential linear/nonlinear relationships.

2. 📉 Histograms

• Purpose: To analyze the distribution of individual numerical variables.
• Insight: Allowed us to detect skewness, outliers, and the spread of the data.

3. 🔗 Correlation Heatmap

• Purpose: To measure the correlation between all numerical variables.
• Insight: Highlighted which features had the strongest linear relationships with Power.

4. 🧠 Hebbian Plot

• Purpose: To visualize potential associations between input variables and output using neuro-inspired representations.
• Insight: Provided a unique perspective on how input features might influence power generation.

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📌 Key Takeaways

• Features such as windspeed and temperature show significant impact on power output.
• Several features exhibit multicollinearity, which may be important for feature selection in modeling.
• Hebbian visualizations introduced an alternative method of interpreting feature importance.