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Long-term solar radiation forecasting in India using EMD, EEMD, and advanced machine learning algorithms.

T RajasundrapandiyanLeebanon¹, N S Sakthivel Murugan², K Kumaresan³

¹Department of Electrical and Electronics Engineering, TamilNadu College of Engineering, Coimbatore, Tamil Nadu, India. leebanon2003@gmail.com.

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|February 18, 2025

Summary

Accurate long-term solar radiation forecasting is crucial for renewable energy and environmental sustainability. Ensemble Empirical Mode Decomposition (EEMD) combined with Multilayer Perceptron (MLP) machine learning models shows superior performance for solar energy prediction.

Keywords:

Empirical mode decomposition Ensemble empirical mode decomposition Indian cities Long-term solar radiation Machine learning

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Area of Science:

Environmental Science
Renewable Energy
Data Science

Background:

Solar radiation is vital for terrestrial carbon sequestration and environmental sustainability.
Accurate solar radiation forecasting is essential for optimizing renewable energy systems.

Purpose of the Study:

To develop and evaluate advanced methods for long-term solar radiation forecasting.
To compare the efficacy of Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) when integrated with various machine learning algorithms.

Main Methods:

Utilized 13 years of meteorological data from seven Indian locations (2000-2012).
Integrated EMD and EEMD with machine learning models: Multilayer Perceptron (MLP), Random Forest Regression (RFR), Support Vector Regression (SVR), and Ridge Regression.
Evaluated model performance using correlation coefficient (R), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE).

Main Results:

EEMD-based machine learning models consistently outperformed EMD-based approaches.
The EEMD integrated with MLP model demonstrated the highest accuracy across all tested locations.
Achieved excellent performance metrics: RMSE = 0.332, MAE = 0.26, and R² = 0.99.

Conclusions:

The proposed EEMD-ML approach offers superior accuracy for long-term solar radiation forecasting compared to existing methods.
This advanced forecasting technique can significantly benefit renewable energy management and environmental sustainability efforts.