Present and Future of AI in Renewable Energy Domain: A Comprehensive Survey
Identifies nine AI-based methodologies for renewable energy systems and benchmarks deep learning vs. conventional models across forecasting, grid control, and optimization
Present and Future of AI in Renewable Energy Domain: A Comprehensive Survey
Abstract
The paper provides a comprehensive review of artificial intelligence applications in renewable energy sectors. It identifies nine AI-based strategies supporting modern power systems through data-driven statistical learning approaches. The survey examines AI techniques across renewable energy generation, forecasting, and system optimization, demonstrating superior performance over conventional models in controllability, data management, cybersecurity, smart grid implementation, and operational efficiency.
Key Contributions
- Nine AI-based methodologies identified for renewable energy applications in contemporary power systems
- Performance comparison of deep learning and machine learning approaches across multiple renewable energy domains
- Systematic analysis of strengths and limitations across different intelligent system implementations
- Integration framework examining AI's role in grid stability, energy forecasting, and resource optimization
- Future directions outlined for sustainable energy transition through advanced AI technologies
- Identification of challenges including infrastructure, skilled personnel, and regulatory barriers to adoption
Methodology
Structured literature review analyzing AI applications across three primary domains:
- Renewable energy generation using deep learning architectures
- Energy forecasting via machine learning and neural network models
- System optimization employing hybrid AI approaches
Evaluates nine distinct methodologies including autoencoders, LSTM networks, CNN-LSTM models, random forests, gradient boosting, and time-delay neural networks. Performance metrics include MSE, RMSE, and classification accuracy across wind, solar, and smart grid datasets.
Results
| Methodology | Performance | Application |
|---|---|---|
| AB-Net (AE+BiLSTM) | MSE: 0.0004 (wind) | Renewable generation forecasting |
| Linear Regression | 96% accuracy | Smart grid stability prediction |
| ENSEMBLE Model | RMSE: 2327 kW | Wind power day-ahead forecasting |
| VOA Algorithm | MAE: 0.2417 (winter) | Solar radiation prediction |
| TDNN | 18.72% income increase | Energy community management |
| CNN-LSTM | >99% accuracy | Transient stability detection |
- AI-powered systems learn from data trends to optimize energy use in real time via integrated sensor networks
- Building energy optimization achieved 35% reduction in energy usage through genetic algorithm approaches
- Deep learning models consistently outperform conventional statistical models across wind, solar, and grid tasks
Limitations
- Data quality issues: Suboptimal sensors and measurement inconsistencies compromise system performance
- Infrastructure challenges: Outdated power system architecture impedes modernization and AI integration
- Skill gaps: Shortage of AI expertise among decision-makers and practitioners in energy sectors
- Economic barriers: High implementation costs and resource requirements limit adoption in developing regions
- Security concerns: AI systems present "black box" transparency issues and increased cybersecurity vulnerabilities
- Real-world application gaps: Limited deployment of AI solutions for extreme weather conditions and fault detection
- Scalability questions: Generalization of models across different geographic locations remains uncertain
Source: Present and Future of AI in Renewable Energy Domain: A Comprehensive Survey by Rashid et al., Westcliff University / Clark Atlanta University