marouaneoa/DTIAEL

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 # Day Type Identification of Algerian Electricity Load **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