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:
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Time Series Data Handling
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Feature Engineering for Demand Forecasting
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Machine Learning (XGBoost) for Prediction
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Model Evaluation (RMSE, MAE)
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Understanding Energy Consumption Patterns
We will make use of :
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Python: The core programming language
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Pandas: Data manipulation and analysis
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NumPy: Numerical operations
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Matplotlib & Seaborn: Data visualization
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Scikit-learn: Machine learning utilities
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XGBoost: Gradient Boosting for robust predictions
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Holidays: For national holiday data
Master Energy Forecasting: A Python Project for Electricity Demand Prediction.
Thanks all students !
