bisnu16cse039/Electric-Power-Consumption

Forcast electric power consumption at Tetouan city in Morocco # Electric-Power-Consumption ## Overview This project focuses on analyzing and predicting forcast electric power consumption at Tetouan city in Morocco using xgboost and transformers. By leveraging historical data, the aim is to develop models that can forecast future power usage patterns, aiding in efficient energy management. ## Dataset The analysis utilizes the "Electric Power Consumption" dataset from Kaggle. This dataset comprises over 50 thousands measurements of electric power consumption energy consumption of the Tétouan city in Morocco over nearly one years. Key features include: - **Date Time**: Time window of ten minutes. - **Temperature**: Weather Temperature. - **Humidity**: Weather Humidity. - **Wind Speed**: Wind Speed. - **Zone 1 Power Consumption** - **Zone 2 Power Consumption** - **Zone 3 Power Consumption** *Note:Therer are no missing values. So we didn't require handle such case. ## Project Structure The repository is organized as follows: - `data/`: Contains the dataset and any data preprocessing scripts. - `notebooks/`: Jupyter notebooks detailing the exploratory data analysis (EDA) and model development. - `src/`: Saved models and training script. - `README.md`: Project overview and instructions. ## Installation To replicate this analysis, ensure you have Python 3.x installed. Follow these steps: 1. **Clone the repository**: ```bash git clone github.com cd Electric-Power-Consumption ``` 2. **Create a virtual environment** (optional but recommended): ```bash python3 -m venv venv source venv/bin/activate # On Windows, use venv\Scripts\activate ``` 3. **Install the required packages**: ```bash pip install -r requirements.txt ``` *Note: Ensure that the `requirements.txt` file lists all necessary dependencies.* ## Usage After setting up the environment: 1. **Data Preprocessing**: Run the data preprocessing script to handle missing values and perform necessary transformati …