🔆 Solar Energy Prediction using SL Transformer and LSTM

Exploring Time-Series Forecasting with Two Powerful Deep Learning Models —Spatial-Temporal Transformer (SL Transformer) and LSTM (Long Short-Term Memory)—to predict solar energy generation using historical and weather data. The SL Transformer is designed to handle both short-term (next 6 hours) and long-term (next few months) predictions, while LSTM is used as a baseline model for comparison.

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📌 Project Overview

This project builds a dual-scale solar energy forecasting system using cutting-edge deep learning models:

🔹 A Spatial-Temporal Transformer (SL Transformer) that captures both local fluctuations and long-term seasonal patterns
🔸 An LSTM model that serves as a traditional baseline for long-range forecasting

Whether it's predicting the next 6 hours or the next 6 months, this system translates solar trends into actionable insights — ideal for smart grids, green energy systems, and future-ready infrastructure.

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🧠 Models Breakdown

🔮 SL Transformer

• Built for multi-horizon forecasting.
• Captures spatial and temporal dependencies using attention mechanisms.
• Predicts:
  • ✅ Next 6 hours (real-time applications)
  • ✅ Next few months (seasonal trend analysis)

⏳ LSTM

• Handles sequential dependencies over long ranges.
• Used for long-term solar trend prediction.
• Serves as a baseline for benchmarking the Transformer model.

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🛰️ Dataset

• 📂 Location: /data
• ✅ Features:
  • Solar irradiance components
  • Meteorological variables (temperature, humidity, wind speed)

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📈 Performance Metrics

Model	MSE (kWh)	MAE (kWh)
LSTM	0.0015	0.0157
SL Transformer	0.0067	0.0469

While LSTM demonstrates stronger performance on long-term forecasting with lower error metrics, the SL Transformer stands out for its ability to generalize across both short-term (next 6 hours) and long-term (next few months) predictions within a single architecture. It captures temporal dependencies and spatial dynamics, making it more scalable and adaptive for real-world solar energy systems where conditions change rapidly. Its ability to learn complex spatial-temporal patterns makes it highly adaptable for real-time energy management, grid optimization, and scalable deployment in smart energy systems.

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🧰 Tech Stack

• 🐍 Python 3.12
• 📦 TensorFlow / PyTorch
• 📊 Pandas, NumPy, Scikit-learn
• 📈 Matplotlib, Seaborn
• 🚀 Google Colab (for training + inference)

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📫 Contact

Indu Sree.N
📧 indusreen78@gmail.com
🐙 GitHub

If you have ideas, suggestions, or just want to connect—feel free to reach out!