AI-Driven Predictive Maintenance for Solar PV Systems

📌 Project Overview

This project uses Machine Learning and AI to predict failures in solar PV systems before they occur, reducing downtime and optimizing energy output.

🔍 Problem Statement

Solar PV systems require continuous monitoring to detect anomalies, reduce operational costs, and prevent unexpected failures. This project builds a predictive maintenance system using real-time sensor data to: ✅ Identify performance anomalies
✅ Forecast potential equipment failures
✅ Provide data-driven insights for proactive maintenance

🛠️ Tech Stack

• Python (Pandas, NumPy, Scikit-Learn, XGBoost, PyTorch)
• SQL (PostgreSQL/SQLite for structured data storage)
• Power BI / Tableau (for dashboards)
• FastAPI / Flask (for ML model deployment)
• AWS / GCP / Azure (for cloud deployment, optional)
• Kafka / Spark (for real-time streaming, optional)
• DVC / MLflow (for model versioning & tracking)

🚀 Project Workflow

1️⃣ Data Collection & Preprocessing
2️⃣ Exploratory Data Analysis (EDA)
3️⃣ Train ML Models (LSTM, XGBoost, Prophet)
4️⃣ Deploy via API (FastAPI)
5️⃣ Create Interactive Dashboard (Power BI/Tableau)
6️⃣ Automate & Deploy in Cloud (AWS/GCP)

📂 Repository Structure

AI-Predictive-Maintenance-Solar-PV/
│── data/              # Raw and processed datasets
│── notebooks/         # Jupyter notebooks for EDA and modeling
│── models/           # Trained ML models
│── scripts/          # Data preprocessing and inference scripts
│── api/              # FastAPI/Flask for model deployment
│── dashboard/        # Power BI/Tableau dashboards
│── config/           # Configuration settings for models and DB
│── logs/             # Logging and monitoring output
│── tests/            # Unit tests for ML pipeline and API
│── README.md         # Project overview

🏆 Expected Outcomes

• Improved solar PV uptime & efficiency
• Real-time anomaly detection & alerts
• Reduced maintenance costs via AI-driven forecasting