Dynamic Model Selection in a Hybrid Ensemble Framework for Robust Photovoltaic Power Forecasting.

Area of Science:

• Renewable Energy Systems
• Artificial Intelligence in Energy

Background:

• Increasing global electricity demand and environmental concerns drive the adoption of renewable energy sources.
• Solar energy offers a cost-effective and deployable solution, but its intermittent nature poses forecasting challenges.
• Accurate solar power prediction is crucial for grid stability and efficient energy management.

Purpose of the Study:

• To develop a novel Flexible Hybrid Ensemble (FHE) framework for enhanced solar power forecasting.
• To dynamically select the optimal base model using prediction error patterns, improving forecast accuracy.
• To provide a robust and scalable forecasting solution for small-scale distributed solar power systems.

Main Methods:

• Proposed a Flexible Hybrid Ensemble (FHE) framework utilizing a meta-model for dynamic base model selection.
• Evaluated the FHE framework using real-world data from four solar power plants.
• Benchmarked the FHE framework against state-of-the-art models and conventional hybrid ensemble techniques.

Main Results:

• The FHE framework demonstrated superior predictive performance, achieving a 30% improvement in Mean Absolute Percentage Error (MAPE) over the SVR model.
• The FHE model maintained high accuracy across diverse weather conditions.
• The framework eliminated the need for preliminary validation of base and ensemble models, simplifying deployment.

Conclusions:

• The proposed FHE framework offers a robust and scalable solution for solar power forecasting.
• Dynamic model selection based on error patterns enhances prediction accuracy and reliability.
• The FHE framework streamlines the deployment process for distributed solar power systems.