Intelligent Solar Energy Generation Forecasting and Agentic Grid Optimization System

Project Overview

This project aims to design and implement an AI-driven solar energy forecasting and grid optimization system. The system predicts solar power generation using historical and weather-related data and extends this capability into an agentic AI assistant that generates structured, explainable recommendations for grid optimization and energy utilization.

The project is developed in two milestones:

• Milestone 1: Machine Learning–based Solar Energy Forecasting
• Milestone 2: Agentic AI Grid Optimization Assistant

The final application is publicly hosted, uses only free-tier/open-source tools, and provides a user-friendly web interface.

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Project Demo

Demo Video: Demo video (Google Drive)

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Problem Statement & Use Case

Solar energy generation is highly variable due to weather conditions and seasonal patterns. Grid operators require accurate forecasts and intelligent decision support to:

• Balance supply and demand
• Reduce energy wastage
• Improve renewable energy integration
• Plan storage and load shifting strategies

This project provides:

• Accurate short-term/long-term solar generation forecasts
• Automated analysis of variability and risk
• AI-generated, structured grid optimization recommendations

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Overall System Architecture

High-Level Architecture

User (Browser)
   │
   ▼
Streamlit Web UI
   │
   ├── Data Upload & Selection
   ├── Forecast Horizon Selection
   ├── Visualization (Graphs & Metrics)
   │
   ▼
Backend Analytics Layer (Python)
   │
   ├── Data Preprocessing & Feature Engineering
   ├── ML / Time-Series Forecasting Models
   ├── Model Evaluation (MAE, RMSE)
   │
   ▼
Forecast Outputs (Structured Data)
   │
   ├── Visualization Module
   └── Agentic AI System (Milestone 2)
           │
           ├── Forecast Analysis Agent
           ├── Variability & Risk Detection Agent
           ├── Knowledge Retrieval Agent (RAG)
           └── Optimization Recommendation Agent
                   │
                   ▼
           Structured Grid Optimization Report

Note: The agentic AI system provides decision-support recommendations only and does not directly control any grid infrastructure.

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Milestone 1: ML-Based Solar Energy Forecasting (Mid-Sem)

Objective

Build a machine learning or time-series forecasting system to predict solar energy generation.

Inputs

• Historical solar power generation data
• Weather indicators (irradiance, temperature, cloud cover)
• Time-based features (hour, day, month, season)

Functional Requirements

• Data preprocessing and cleaning
• Feature engineering
• Solar energy forecasting
• Trend and seasonality analysis
• Visualization of predictions

Technical Requirements

• ML or time-series models such as:

  • Linear Regression
  • Random Forest Regressor
  • ARIMA / SARIMA
  • Prophet (optional)

• Evaluation metrics:

  • MAE (Mean Absolute Error)
  • RMSE (Root Mean Square Error)

UI Requirements

• Dataset upload or selection
• Forecast horizon selection
• Line plots (Actual vs Predicted)
• Trend and seasonality visualizations

Milestone 1 Deliverables

• Problem understanding & use-case description
• Input–output specification
• Forecasting pipeline architecture
• Working application with basic UI
• Forecast accuracy evaluation

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Milestone 2: Agentic AI Grid Optimization Assistant (End-Sem)

Objective

Extend the forecasting system into an agentic AI assistant that reasons about forecast variability and generates structured grid optimization recommendations.

Functional Requirements

• Analyze forecast outputs and uncertainty
• Identify variability and risk periods
• Retrieve renewable energy and grid management guidelines
• Generate structured optimization recommendations
• Handle incomplete or uncertain data gracefully

Technical Requirements

• Open-source or free-tier LLM integration
• Agentic workflow with explicit state management (LangGraph)
• Retrieval-Augmented Generation (optional but recommended)
• Prompt strategies to avoid unsupported claims

Structured Output Report

The generated report includes:

• Solar generation forecast summary
• Identified variability and risk periods
• Grid balancing and storage recommendations
• Energy utilization optimization strategies
• Supporting references

Optional Extensions

• Battery storage optimization analysis
• Multi-site solar forecasting
• PDF export of optimization report
• Scenario-based energy planning

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Dataset

• Solar Energy Power Generation Dataset
• Source: Kaggle
• Link: https://www.kaggle.com/datasets/stucom/solar-energy-power-generation-dataset
• File: data/spg.csv
• Records: 4,213 entries
• Features: 21 columns including:
  • Weather indicators: temperature, humidity, pressure, precipitation, cloud cover
  • Solar radiation: shortwave radiation backwards surface
  • Wind data: speed and direction at multiple altitudes (10m, 80m, 900mb)
  • Solar geometry: angle of incidence, zenith, azimuth
  • Target variable: generated_power_kw

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Tech Stack & Tools

Core Technologies

Layer	Tools / Libraries	Use Case
Language	Python 3.12+	Core development
Data Processing	Pandas, NumPy	Data cleaning & feature engineering
ML Models	scikit-learn	Regression models
Time Series	statsmodels, Prophet	Seasonal forecasting
Visualization	Matplotlib, Seaborn	Trend & prediction plots
UI	Streamlit	Interactive web interface
LLMs	Open-source models (LLaMA, Mistral, Phi)	Recommendation generation
Agent Framework	LangGraph	Multi-agent workflows
Vector Store	FAISS / Chroma	Knowledge retrieval (RAG)
Hosting	Hugging Face Spaces	Public deployment

No paid APIs are used in this project.

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Project Setup Guide

1️⃣ Clone the Repository

git clone https://github.com/Yashsingh045/Intelligent-Solar-Energy-Generation-Forecasting.git
cd Intelligent-Solar-Energy-Generation-Forecasting

2️⃣ Create Virtual Environment

python -m venv venv
source venv/bin/activate  # Linux / Mac
venv\Scripts\activate     # Windows

3️⃣ Install Dependencies

pip install -r requirements.txt

Key Dependencies:

• streamlit - Web application framework
• pandas - Data manipulation and analysis
• matplotlib - Data visualization
• numpy - Numerical computing
• seaborn - Statistical data visualization
• scikit-learn - Machine learning library
• jupyter - Interactive notebook environment

4️⃣ Run the Application (Local)

streamlit run app.py

The application will open in your browser at http://localhost:8501

5️⃣ Explore the Data (Optional)

jupyter notebook notebooks/data.ipynb

This notebook contains exploratory data analysis including:

• Data quality checks
• Correlation analysis
• Feature visualization
• Statistical summaries

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How to Use the Application

1. Launch the App

   streamlit run app.py

2. Upload Dataset

   • Click "Browse files" or drag and drop your CSV file
   • Maximum file size: 10MB
   • Supported format: CSV with UTF-8 encoding

3. Explore Your Data

   • View dataset preview (first 20 rows)
   • Check dataset shape and column names
   • Select any numeric column from the dropdown
   • View interactive line plots

4. Analyze Patterns

   • Use the notebook for deeper analysis
   • Examine correlations between features
   • Identify key predictors of power generation

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Deployment

The application is deployed using Hugging Face Spaces (Streamlit):

• Free-tier hosting
• Publicly accessible URL
• Automatic build from repository

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Future Improvements

• Integration of real-time weather data (free APIs)
• Advanced deep learning models (LSTM, Temporal CNNs)
• Enhanced uncertainty quantification
• Real-world grid simulation scenarios

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Project Contributors

• Yashveer Singh
• Abhay Pratap Yadav
• Vipul
• Ananya Gupta

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✨ This project demonstrates the intersection of machine learning, renewable energy analytics, and agentic AI systems to support sustainable power grid operations.