Machine Learning Project
This project analyzes global solar and hydroelectric energy consumption trends across different countries, with a focus on identifying relationships with GDP and population metrics. Using various machine learning models, the project predicts solar energy consumption and energy prices, aiming to provide insights that support the shift toward renewable energy solutions. This project also integrates synthetic data and applies cross-validation techniques to improve model accuracy.
Visualize global energy consumption trends in solar and hydroelectric sectors. Compare energy consumption patterns with economic indicators like GDP and population. Forecast future solar consumption using machine learning models. Predict energy prices using different predictive models. Enhance prediction accuracy with synthetic data and cross-validation techniques.
The project utilizes datasets containing:
Country-wise energy consumption data, including solar, hydroelectric usage, GDP, and population metrics. Energy data by Indian state, including energy generation and price metrics.
This study highlights the effectiveness of machine learning in renewable energy forecasting, with ensemble models (Random Forest, Gradient Boosting) outperforming traditional models. The integration of synthetic data and cross-validation further enhances the reliability of predictions. These findings can inform energy policy and support a data-driven transition toward sustainable energy use globally.
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\SolarEnergy:
Contains datasets and Jupyter notebooks used for the analysis and model training of solar energy consumption and prices across multiple countries. -
\Datasets:
Contains saved datasets specifically for solar consumption and price prediction. -
\IndianSolar:
Contains datasets and Python code related to the analysis of solar consumption and prices within India.
git clone https://github.com/Shri21cyb/SolarEnergy_Statistics.git
Run Jupyter Notebooks: Open the notebooks in /notebooks to see data preprocessing, model training, and analysis steps.
Python 3.7+ Libraries: pandas, numpy, scikit-learn, matplotlib, seaborn, xgboost
Further research can expand on this project by:
Incorporating real-time energy data for improved forecasting. Extending analysis to include additional renewable sources like wind and biomass. Developing more detailed geographic-based energy consumption analysis.