Comparative Table: AI-based Solar Energy Research Papers
Here's a comparative table summarizing and contrasting the key features, advantages, and disadvantages of six AI-based solar energy research papers.
| Aspect | Paper 1: RELAD-ANN (Hanif et al., 2024) | Paper 2: Gen-AI (Mousavi et al., 2025) | Paper 3: Forecasting & Grid (Bouquet et al., 2024) | Paper 4: AI in PV Tech (Mohammad et al., 2023) | Paper 5: AI-Holistic Grid (Wen et al., 2024) | Paper 6: AI for RE Systems (Bennagi et al., 2024) |
|---|---|---|---|---|---|---|
| Focus | Solar irradiance forecasting with novel ANN | Generative AI for solar forecasting & design | LSTM-based solar forecasting + smart grid integration | Broad AI applications across PV design & monitoring | Holistic AI for generation, MPPT, DSM, VPPs | AI in solar, wind, hydro, microgrids (Review) |
| AI Models Used | RELAD-ANN, LightGBM, SVR, LSIPF | GANs, VAEs, RL, cGANs | LSTM, preprocessing pipeline, CNN | ML, deep learning, image-based AI | SVR, ANN, LSTM, CNN, RL, DRL, Autoencoders | ANN, SVM, BPNN, RBFNN, ANFIS, etc. |
| Data Scope | Real hourly irradiance (Quetta, Pakistan) | Conceptual designs, no new dataset | 6-year GHI data (EPFL), 10s resolution | General narrative, not experimental | Multiple simulation datasets across use cases | Literature review, multiple case mentions |
| Performance | R² = 0.935, MAE = 8.20, MAPE = 3.48% | No metrics; theory-based | RMSE, MAE, R², excellent LSTM results across 15 horizons | No benchmarks | Extensive metrics (e.g., MAPE, RMSE, DRL cost savings, load smoothing) | No performance metrics, conceptual analysis |
| Strengths | Practical, high-accuracy forecasting; compared with 6 models | Innovative Gen-AI integration; novel use cases | Cleaned open dataset, multi-horizon, real deployment focus | Broad, touches materials to smart grids | End-to-end: MPPT, fault detection, demand-side mgmt, cybersecurity | Diverse scope; identifies industry trends and gaps |
| Weaknesses | Limited geographic generalization | Lacks implementation details and empirical support | High complexity; heavy reliance on data preprocessing | Repetitive, lacks structured evaluation | Scalability, complexity, requires large computing power | Not experimental; limited technical depth |
| Methodology | Hybrid AI for real-time forecasting in solar applications | Conceptual Gen-AI models for solar generation and optimization | LSTM modeling for forecasting, smart grid integration using AI | A survey on AI approaches in PV system design, optimization, and monitoring | Deep integration of AI in grid management from generation to consumption | Comprehensive review of AI techniques in renewable energy systems |
| Target Audience | Solar researchers, energy forecasting modelers | AI researchers, energy system designers, future-forward solutions | Researchers working on smart grids, solar energy forecasting | PV designers, solar panel manufacturers, and researchers in the field | Researchers, utilities, energy management companies | Researchers, policymakers, energy consultants |
| Data-Driven Approach | Empirical data analysis (real field data) | Conceptual, no real data integration | Data-intensive with real-world data, focused on scalability | Theoretical, lacks empirical data | Extensive simulation-based, covering multiple energy systems | Literature review, covers multiple case studies |
| Use Cases | Solar irradiance forecasting, energy production estimation | Conceptual framework for solar forecasting, energy system design | Solar energy forecasting, grid integration, and forecasting models | PV design optimization, fault diagnosis, PV system performance | Smart grid operations, optimization of MPPT, demand-side management | Comparative analysis of AI techniques in solar, wind, and hydro |
| Unique Contributions | Combines multiple AI models for improved forecasting accuracy | Introduces generative AI methods for energy system design | Real-world, multi-horizon forecasting integrated with grid management | Focus on broad applications and lifecycle management of PV systems | Comprehensive approach to AI applications across grid management | Extensive review of AI in renewable energy, useful for new research |
Expanded Narrative Insights per Paper
1. RELAD-ANN (Hanif et al., 2024)
- Best for: High-accuracy forecasting
- Unique Strength: Combines ANN + LightGBM
- Limitation: Based on Quetta region
2. Gen-AI (Mousavi et al., 2025)
- Best for: Generative AI in renewables
- Unique Strength: Uses GANs, VAEs
- Limitation: No experimental validation
3. Forecasting & Grid (Bouquet et al., 2024)
- Best for: Forecasting + smart grid
- Unique Strength: Public dataset, real deployment
- Limitation: Complex preprocessing
4. AI in PV Tech (Mohammad et al., 2023)
- Best for: AI in full PV lifecycle
- Unique Strength: Covers design to maintenance
- Limitation: No experimental results
5. Holistic AI Grid (Wen et al., 2024)
- Best for: Full smart grid AI solutions
- Unique Strength: MPPT to cybersecurity
- Limitation: Requires high computing
6. AI for RE Systems (Bennagi et al., 2024)
- Best for: Review of AI in renewables
- Unique Strength: 150+ studies analyzed
- Limitation: No new methodology
Future Scope and Recommendations
Hybrid Modeling
Combine generative + predictive models for improved forecasting.
Geographical Diversification
Use data from diverse climates for better generalization.
Standardized Metrics
Enable fair comparisons using unified performance metrics.
Empirical Validation
Test Gen-AI models using real-world experiments.
AI Interpretability
Apply XAI to increase AI system trust and transparency.
Cross-sector Integration
Incorporate market, weather, and policy signals for better AI decisions.
These actions aim to move AI in solar and smart grids toward real-world deployment.