Machine Learning Project – Electricity Demand Forecasting

In this project, you will learn how to build a Machine Learning model with Python. We will build a XGBoost Model that will help us in forecasting of electricity demand in a city.

You will learn how to handle time-series data, create powerful features, train a machine learning model and and evaluate its performance.

Here, we have used a historical data of last 5 years. Based on this data we will predict the future demand using our model.

This is a time series dataset with Per Hour information. In this dataset, we have multiple useful columns like – Temperature, Humidity, Demand etc.

From the datetime column, we created other useful columns like day_of_year, week_of_year, is_weekend, is_holiday etc.

We have used the line chart, box plot for visualization.

Key Learnings:

• Time Series Data Handling
• Feature Engineering for Demand Forecasting
• Machine Learning (XGBoost) for Prediction
• Model Evaluation (RMSE, MAE)
• Understanding Energy Consumption Patterns

We will make use of :

• Python: The core programming language
• Pandas: Data manipulation and analysis
• NumPy: Numerical operations
• Matplotlib & Seaborn: Data visualization
• Scikit-learn: Machine learning utilities
• XGBoost: Gradient Boosting for robust predictions
• Holidays: For national holiday data

Master Energy Forecasting: A Python Project for Electricity Demand Prediction.

Thanks all students .