Introduction:

This Jupyter notebook explores the identification of different day types based on electricity load patterns in an Algerian city. We will analyze a dataset containing hourly recordings of Maximum Power Demand (PMA) and Temperature for two years, from January 1st, 2016, to December 31st, 2017. By applying data mining techniques, we aim to:

• Preprocess and explore the data:
  • Analyze descriptive statistics and visualize trends in PMA and Temperature.
  • Extract additional features from the date information, including day of week, week of year, month, and holiday identification.
• Identify day types:
  • Implement and compare clustering algorithms, such as K-means and Hierarchical clustering, to group days based on their similarity in PMA and Temperature patterns.
  • Evaluate the performance of each algorithm using metrics like silhouette score and Calinski-Harabasz index.
• Analyze and interpret the results:
  • Characterize the identified day types based on their PMA and Temperature distributions.
  • Discuss potential implications and applications of the findings for electricity load forecasting and grid management.

Data Source:

The dataset used in this analysis is stored in a file named pma.xlsx. It contains three columns:

• time: Date and time (including hour)
• pma: Maximum Power Demand (MW)
• tmp: Temperature (°C)

Software and Tools:

This project will utilize Python libraries such as:

• pandas for data manipulation and analysis
• numpy for scientific computing
• matplotlib and seaborn for data visualization
• scikit-learn for machine learning and clustering algorithms