https://github.com/sayande01/predicting-future-energy-consumption-using-xgboost

"Using XGBoost to predict future energy consumption (in MW) based on historical data. This project includes extensive feature engineering and identifies key factors driving consumption patterns through feature importance analysis, enabling precise, data-driven forecasting."
https://github.com/sayande01/predicting-future-energy-consumption-using-xgboost

time-series-forecasting xgboost-regression

Last synced: 2 months ago
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"Using XGBoost to predict future energy consumption (in MW) based on historical data. This project includes extensive feature engineering and identifies key factors driving consumption patterns through feature importance analysis, enabling precise, data-driven forecasting."

• Host: GitHub
• URL: https://github.com/sayande01/predicting-future-energy-consumption-using-xgboost
• Owner: sayande01
• Created: 2024-11-12T08:51:09.000Z (7 months ago)
• Default Branch: main
• Last Pushed: 2024-11-12T08:54:21.000Z (7 months ago)
• Last Synced: 2025-02-13T02:38:38.199Z (4 months ago)
• Topics: time-series-forecasting, xgboost-regression
• Language: Jupyter Notebook
• Homepage:
• Size: 1.47 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Predicting Future Energy Consumption Using XGBoost

## Description

This project aims to forecast hourly energy consumption (in megawatts, MW) by analyzing historical consumption data from the American Electric Power dataset. Using machine learning techniques, primarily XGBoost, this model predicts future energy demand based on past trends, with extensive exploration of the dataset and careful feature engineering. The project includes visualizations to understand consumption patterns, distribution analysis, and model evaluation to optimize prediction accuracy.

## Objective

The primary objective is to build an accurate predictive model that forecasts energy consumption, aiding energy providers in managing resources efficiently, reducing operational costs, and planning for sustainable energy usage.

## Project Workflow

1. **Data Loading and Exploration**:

   - Data on hourly consumption from AEP was loaded and processed, with `Datetime` as the index for time series analysis.

   - Initial exploration identifies data distribution, trends, and potential missing values.

2. **Exploratory Data Analysis (EDA)**:

   - Histograms and boxplots are used to examine the distribution of energy consumption.

   - Time series plots reveal consumption trends, highlighting peak and off-peak periods.

3. **Feature Engineering**:

   - Time-based features such as hour, day of the week, month, and seasonality indicators are created to capture temporal patterns.

   - Additional transformations include rolling averages and lag features to enhance the model’s predictive power.

4. **Modeling with XGBoost**:

   - The dataset is split into training and testing sets.

   - An XGBoost regression model is trained to predict hourly consumption, with hyperparameter tuning to improve accuracy.

5. **Feature Importance Analysis**:

   - XGBoost’s feature importance attributes help identify the most impactful features, giving insight into key drivers of energy demand.

6. **Model Evaluation**:

   - Performance is measured using metrics like Mean Squared Error (MSE) to ensure reliability and precision in predictions.

   - Visualizations of predicted vs. actual values demonstrate the model’s effectiveness.

## Conclusion

This project successfully demonstrates how XGBoost can be applied to time series data for accurate energy consumption forecasting, providing valuable insights for energy sector stakeholders in managing and optimizing resources.

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