A Review on Machine Learning Applications for Solar Plants.

Area of Science:

• Renewable Energy Systems
• Artificial Intelligence in Engineering
• Solar Power Technology

Background:

• Solar plant systems exhibit complex nonlinear dynamics and uncertainties, challenging conventional algorithmic approaches.
• Classical methods often fall short in providing the necessary safety, reliability, and performance for modern solar plants.
• Machine Learning (ML) has emerged as a critical technology for enhancing solar plant operations.

Purpose of the Study:

• To explore and analyze Machine Learning (ML) technologies for solar plant design, forecasting, maintenance, and control.
• To compare the advantages and shortcomings of ML methods against classical approaches in solar energy applications.
• To propose an integration scheme for ML sensor systems and outline the digital transformation towards smart solar plants.

Main Methods:

• Review and analysis of existing ML technologies applicable to solar plant systems.
• Development and summarization of intelligent, self-adaptive ML models for sizing, forecasting, maintenance, and control.
• Benchmarking ML model performance for solar plant systems and proposing an integration scheme.

Main Results:

• ML models demonstrate superior performance in safety, reliability, robustness, and overall efficiency for solar plants.
• Established performance benchmarks for various ML models in solar plant applications.
• Proposed a practical integration scheme for ML sensor systems, facilitating digital transformation.

Conclusions:

• ML technologies are essential for overcoming the complexities of solar plant dynamics and improving operational efficiency.
• The proposed integration scheme provides a pathway for developing smart solar plants with enhanced capabilities.
• ML adoption is projected to significantly impact the solar plant value chain, driving innovation and sustainability.