Analysis of variables to determine their influence on renewable energy forecasting using ensemble methods

Area of Science:

• Renewable Energy Systems
• Computational Intelligence
• Data Science

Background:

• Effective energy forecasting is vital for managing renewable energy sources and ensuring grid stability.
• Selecting the optimal prediction system for solar energy is complex due to variations in energy infrastructure.
• Ensemble methods offer a robust approach to improving forecasting accuracy in variable conditions.

Purpose of the Study:

• To investigate the influence of sampling frequency, neural network architecture, and ensemble method depth on solar energy prediction.
• To identify the most effective solar energy prediction models for diverse geographical locations and energy infrastructures.
• To provide a framework for selecting optimal systems for accurate and efficient solar energy forecasting.

Main Methods:

• Utilized ensemble methods for solar energy prediction across multiple locations.
• Analyzed the impact of varying sampling frequencies for solar panel systems.
• Evaluated different neural network architectures and the number of ensemble blocks per model.

Main Results:

• Identified location-specific optimal solar energy prediction models.
• Demonstrated that the use of short time intervals is a critical factor for accurate forecasting.
• The effectiveness of short time intervals was found to be independent of the prediction model type and ensemble method.

Conclusions:

• Short time intervals are essential for accurate solar energy forecasting, irrespective of the chosen prediction model or ensemble technique.
• The study provides a framework for selecting the best solar energy prediction systems tailored to specific infrastructure needs.
• Optimized forecasting enhances the efficient management of solar energy resources.