🌞 AI-Based Solar Forecasting Project Summary

🎯 Project Goal

Develop an AI-based predictive framework using deep learning (LSTM) to enhance forecasting, efficiency, and economic viability of solar energy systems integrated with smart grid infrastructure.

⚙️ Technical Methodology

• Real-time irradiance data (GHI, GHIcs, kcs) @10s frequency
• Forecasting from 15 min to 7 days
• LSTM-based deep learning models
• Evaluation metrics: MAE, RMSE, nMAE, nRMSE, R² (> 0.9)

🌐 Key Outcomes

• Accuracy: 30–50% lower error than traditional models
• Cost Savings: Save Money by get rid of error
• Grid Optimization: 20–40% reduced curtailment

🧠 AI Integration Benefits

• Smart grid automation
• Predictive maintenance via anomaly detection
• Hybrid ML + DL model options
• Scalable to other renewables (wind, hydro)

📈 Sustainability & Planning

• Supports utility & government energy planning
• 500K–1M tons CO₂ reduction annually
• Open data access with 17M+ records shared

🔍 Challenges Solved

• Forecast variability → LSTM time-series learning
• Market penalties → Precise bidding forecasts
• Reserve overuse → Confidence in predictions
• Manual work → Automation pipeline
• Panel loss → AI-based MPPT optimization