AI-Based Smart Solar–Wind Hybrid Energy Management and Predictive Power System
Project Price Starts From ₹20,000
The AI-Based Smart Solar–Wind Hybrid Energy Management and Predictive Power System combines solar and wind energy with battery storage and grid power to provide intelligent energy management. The ESP32 continuously collects solar generation, wind generation, battery voltage, current, SOC, temperature, environmental conditions, and load consumption data.
The collected data is sent to a Python-based AI system through MQTT. Machine learning models analyze historical and real-time data to predict solar/wind generation and future load demand. Based on the prediction and battery condition, the system intelligently manages solar power, wind power, battery charging/discharging, and grid supply. All live and predicted data is displayed through a web-based dashboard.
AI-Based Features
🤖 Solar Power Prediction
🌬️ Wind Power Prediction
📊 Future Load Prediction
🔋 Battery SOC Prediction
🌦️ Weather/Environment-Based Energy Prediction
⚡ Intelligent Energy Source Selection
📈 Energy Consumption Forecasting
🚨 Abnormal Power/Fault Detection
Objectives
✅ Solar & Wind Hybrid Power Monitoring
✅ Real-Time Voltage & Current Measurement
✅ Smart Battery SOC Monitoring
✅ Automatic Battery/Grid Switching
✅ AI-Based Solar Generation Prediction
✅ AI-Based Wind Generation Prediction
✅ Future Load Prediction
✅ Intelligent Energy Management
✅ IoT Web Dashboard
✅ Historical Data Logging & Analytics
Web Dashboard Features
☀️ Solar Generation
🌬️ Wind Generation
🔋 Battery SOC & Voltage
⚡ Load Consumption
🌐 Grid Status
🌡️ Environmental Data
🤖 AI Prediction Results
📊 Live & Historical Graphs
🔄 Automatic Source Switching
🚨 Fault & Alert Notifications
📈 Daily/Monthly Energy Reports
Applications
🏠 Smart Homes
🏭 Industrial Energy Management
🌾 Agricultural Farms
⚡ Smart Microgrids
🏢 Commercial Buildings
☀️ Solar-Wind Power Plants
🔋 Battery Energy Storage Systems
🎓 Renewable Energy Research
📦 Includes:
✅ Complete Source Code • AI Model • IoT Web Dashboard • Circuit Diagram • Documentation • Project Report • MQTT Integration • Prediction Module • Installation Guide • User Manual • Technical Support