A survey on advanced machine learning and deep learning techniques assisting in renewable energy generation

Area of Science:

• Renewable Energy Systems
• Artificial Intelligence in Energy

Background:

• The increasing integration of renewable energy necessitates accurate forecasting for grid management.
• Reliable energy generation predictions are crucial for grid stability, cost-efficiency, and risk mitigation in energy markets.

Purpose of the Study:

• To provide a comprehensive review of machine learning (ML) and deep learning (DL) techniques for solar irradiation forecasting.
• To analyze existing ML/DL methods and metaheuristic optimization for renewable energy prediction.

Main Methods:

• Review and analysis of ML and DL algorithms applied to renewable energy forecasting.
• Examination of metaheuristic optimization techniques in the context of renewable energy prediction.

Main Results:

• The study categorizes and analyzes various ML/DL approaches for solar and renewable energy forecasting.
• Identifies current challenges and open issues in renewable energy projection.

Conclusions:

• Machine learning and deep learning offer powerful tools for enhancing the accuracy of renewable energy forecasts.
• Further research into advanced ML/DL models and addressing open challenges is essential for optimizing renewable energy integration.