A Comprehensive Review on Ensemble Solar Power Forecasting Algorithms.

Area of Science:

• Renewable Energy Systems
• Power Grid Management
• Solar Energy Forecasting

Background:

• Increasing energy demand necessitates greater integration of renewable sources like solar power.
• Solar energy's non-stationary and non-linear nature requires accurate forecasting for grid stability.
• Photovoltaic (PV) plant reliability and power supply/demand management depend on precise solar irradiance predictions.

Purpose of the Study:

• To provide an overview of recent studies on solar irradiance forecasting.
• To focus on ensemble methods for solar irradiance prediction.
• To categorize and investigate ensemble forecasting techniques.

Main Methods:

• Review of recent literature on solar irradiance forecasting.
• Classification of ensemble methods into competitive and cooperative categories.
• Analysis of parameter diversity and data diversity in competitive ensembles.
• Investigation of preprocessing and post-processing in cooperative ensembles.

Main Results:

• Ensemble methods offer a promising approach to solar irradiance forecasting.
• Competitive ensemble forecasting utilizes parameter and data diversity.
• Cooperative ensemble forecasting employs preprocessing and post-processing techniques.
• The study investigates various ensemble forecasting methods.

Conclusions:

• Ensemble methods are vital for enhancing the accuracy of solar irradiance forecasts.
• Understanding the different types of ensemble methods (competitive and cooperative) is key to their effective application.
• Further research is recommended to explore advanced ensemble techniques for improved PV plant integration and grid management.