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

## TL;DR

A new hybrid forecasting system for solar power dynamically chooses the best model for each prediction, improving accuracy and reliability.

## Contribution

A flexible hybrid ensemble framework that dynamically selects models for photovoltaic forecasting, improving performance and robustness.

## Key findings

- The FHE framework achieves a 30% improvement in Mean Absolute Percentage Error compared to the SVR model.
- The model maintains high accuracy across diverse weather conditions without requiring preliminary validation.
- Evaluation on four real PV plants in South Korea shows strong generalization across different test sizes and CV splits.

## Abstract

What are the main findings?
A flexible hybrid ensemble is proposed for photovoltaic (PV) power forecasting.The ensemble dynamically selects among diverse models for each prediction instance.

A flexible hybrid ensemble is proposed for photovoltaic (PV) power forecasting.

The ensemble dynamically selects among diverse models for each prediction instance.

What is the implication of the main finding?
The model achieves state-of-the-art performance in both accuracy and robustness.Evaluation on four real PV plants in South Korea shows strong generalization across different test sizes and CV splits.

The model achieves state-of-the-art performance in both accuracy and robustness.

Evaluation on four real PV plants in South Korea shows strong generalization across different test sizes and CV splits.

As global electricity demand increases and concerns over fossil fuel usage intensify, renewable energy sources have gained significant attention. Solar energy stands out due to its low installation costs and suitability for deployment. However, solar power generation remains difficult to predict because of its dependence on weather conditions and decentralized infrastructure. To address this challenge, this study proposes a flexible hybrid ensemble (FHE) framework that dynamically selects the most appropriate base model based on prediction error patterns. Unlike traditional ensemble methods that aggregate all base model outputs, the FHE employs a meta-model to leverage the strengths of individual models while mitigating their weaknesses. The FHE is evaluated using data from four solar power plants and is benchmarked against several state-of-the-art models and conventional hybrid ensemble techniques. Experimental results demonstrate that the FHE framework achieves superior predictive performance, improving the Mean Absolute Percentage Error by 30% compared to the SVR model. Moreover, the FHE model maintains high accuracy across diverse weather conditions and eliminates the need for preliminary validation of base and ensemble models, streamlining the deployment process. These findings highlight the FHE framework’s potential as a robust and scalable solution for forecasting in small-scale distributed solar power systems.