12.AI-Based Smart Classroom Monitoring and Attendance System

AI-Based Smart Classroom Monitoring and Attendance System ESP32 + RFID + AI Agent + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Cloud Dashboard

AI-Based Smart Classroom Monitoring and Attendance System ESP32 + RFID + AI Agent + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Cloud Dashboard 1. Project Overview This project is a complete Smart Classroom Monitoring and Attendance System that automatically: ✅ Records student attendance using RFID cards ✅ Monitors classroom temperature and humidity ✅ Tracks classroom occupancy ✅ Uploads data to cloud ✅ Stores attendance in Google Sheets ✅ Sends Telegram alerts ✅ Generates AI-based insights ✅ Predicts classroom power consumption ✅ Provides voice notifications ✅ Creates a real-time dashboard using ThingSpeak 2. System Architecture Data Flow RFID Card ↓ ESP32 Controller ↓ WiFi Connection ↓ ThingSpeak Cloud ↓ n8n Automation ↓ Google Sheets Database ↓ Telegram Bot ↓ Voice Notification ↓ AI Analysis Agent 3. Features Attendance Monitoring RFID-based attendance Automatic student identification Real-time attendance logging Classroom Monitoring Temperature Monitoring Humidity Monitoring Occupancy Monitoring AI Features Attendance trend analysis Absentee prediction Power consumption prediction Classroom utilization analysis Cloud Features ThingSpeak Dashboard Google Sheets Storage Telegram Notifications Voice Alerts 4. Hardware Components Component Quantity ESP32 Dev Board 1 RFID RC522 Module 1 RFID Cards/Tags Multiple DHT11 Sensor 1 IR Occupancy Sensor 1 OLED Display 0.96" 1 Buzzer 1 LEDs 2 Breadboard 1 Jumper Wires As Required USB Cable 1 Power Supply 5V 5. ESP32 Pin Connections RFID RC522 RC522 ESP32 SDA GPIO5 SCK GPIO18 MOSI GPIO23 MISO GPIO19 RST GPIO22 3.3V 3.3V GND GND DHT11 DHT11 ESP32 VCC 3.3V GND GND DATA GPIO4 IR Sensor IR Sensor ESP32 VCC 3.3V GND GND OUT GPIO27 Buzzer Buzzer ESP32 Positive GPIO26 Negative GND 6. Circuit Schematic +------------------+ | ESP32 | | | RFID RC522 ---> | SPI Interface | DHT11 -------> | GPIO4 | IR Sensor ---> | GPIO27 | Buzzer -----> | GPIO26 | OLED -------> | I2C | +------------------+ | WiFi | Internet Cloud | -------------------------------- | | | Google Sheets Telegram ThingSpeak | | | -------------------------------- | AI Agent 7. Flowchart START | Initialize ESP32 | Connect WiFi | Read RFID Card | Card Detected? | YES | Identify Student | Read DHT11 | Read Occupancy Sensor | Upload Data | Store Attendance | Trigger n8n Workflow | Send Telegram Alert | Generate Voice Message | Update Dashboard | Repeat 8. Attendance Data Format { "student_name":"Rahul", "student_id":"RF001", "attendance":"Present", "temperature":"28", "humidity":"65", "occupancy":"Occupied", "timestamp":"2026-05-31 09:15:00" } 9. ESP32 Source Code Structure Required Libraries WiFi.h HTTPClient.h SPI.h MFRC522.h DHT.h ArduinoJson.h WiFi Configuration const char* ssid = "YOUR_WIFI"; const char* password = "YOUR_PASSWORD"; ThingSpeak API String apiKey = "YOUR_THINGSPEAK_KEY"; Student RFID Mapping String card1 = "D3A12F45"; String student1 = "Rahul"; String card2 = "B4C56789"; String student2 = "Priya"; Main Program Logic Read RFID Read DHT11 Read Occupancy Create JSON Send to: ThingSpeak Webhook Google Sheets Wait 10. ThingSpeak Setup Step 1 Create account on: https://thingspeak.com Step 2 Create New Channel Fields: Field 1 → Student ID Field 2 → Temperature Field 3 → Humidity Field 4 → Occupancy Step 3 Copy: Write API Key Read API Key Channel ID Step 4 Use API in ESP32 https://api.thingspeak.com/update 11. Google Sheets Setup Create Sheet Attendance_Log Columns: Date Time Student Name Student ID Temperature Humidity Occupancy Status 12. n8n Workflow Design Webhook | ▼ Google Sheets Node | ▼ AI Agent Node | ▼ Telegram Node | ▼ Voice Generator 13. n8n Workflow JSON Structure { "nodes":[ { "name":"Webhook" }, { "name":"Google Sheets" }, { "name":"AI Agent" }, { "name":"Telegram" } ] } 14. Telegram Bot Setup Step 1 Open Telegram Search: BotFather Step 2 /newbot Step 3 Create Bot Example: SmartClassroomBot Step 4 Copy Bot Token 123456:ABCXYZ 15. Telegram Alert Example 📚 Smart Classroom Alert Student: Rahul RFID: RF001 Attendance: Present Temperature: 28°C Humidity: 65% Time: 09:15 AM 16. Voice Notification Automation Telegram Voice Message Generated by: Google TTS or OpenAI TTS or ElevenLabs Example: Student Rahul attendance recorded successfully. Classroom temperature is 28 degree Celsius. 17. AI Attendance Analysis The AI Agent analyzes: Daily Attendance Present % Absent % Late % Weekly Trends Most active students Frequent absentees Attendance prediction 18. AI Power Consumption Prediction Logic Inputs Occupancy Temperature Class Duration Fan Usage Light Usage Example Dataset Students Temp Fan Light Power 10 28 ON ON 300W 25 30 ON ON 500W 40 32 ON ON 800W Prediction Formula Power=a(Occupancy)+b(Temperature)+c(FanUsage)+d(LightUsage) AI estimates future classroom power requirements and identifies energy-saving opportunities. 19. AI Agent Prompts Example Prompt: Analyze today's attendance. Provide: 1. Attendance % 2. Absent Students 3. Classroom Utilization 4. Energy Consumption Forecast 5. Recommendations 20. ThingSpeak Dashboard Dashboard Widgets: Attendance Count Temperature Graph Humidity Graph Occupancy Status Power Consumption Prediction Attendance Trend Graph 21. Security Features RFID Authentication Only registered cards accepted. Cloud Security HTTPS APIs Token Authentication Telegram Bot Security Backup Google Sheets cloud backup. 22. Future Enhancements AI Face Recognition Replace RFID with camera attendance. Classroom Behavior Analysis Monitor student engagement. Smart Energy Control Automatically control: Lights Fans Projectors Voice Assistant Classroom AI Assistant. Mobile App Android & iOS Application. 23. Real Deployment Guide Classroom Installation Mount RFID reader near classroom entrance. Install ESP32 controller box. Place DHT11 sensor inside classroom. Install occupancy sensor at door. Connect to WiFi network. Configure ThingSpeak. Configure n8n workflow. Connect Telegram Bot. Test attendance logging. Enable AI analytics. Final Outcome This project creates a complete Industry 4.0 Smart Classroom platform combining: ESP32 IoT Monitoring RFID Attendance Tracking AI Agent Analytics n8n Workflow Automation Google Sheets Database Telegram Voice Alerts ThingSpeak Dashboard Power Consumption Prediction Cloud-Based Monitoring It is suitable for B.Tech, M.Tech, Diploma, Polytechnic, Final Year Engineering, IoT, AI & Embedded Systems projects and can be expanded into a full smart campus solution.

AI Smart Solar Panel Tracking System with Weather Optimization_agent

AI Smart Solar Panel Tracking System with Weather Optimization Agent AI-Powered ESP32 Agentic IoT Solar Tracker using n8n Automation, Telegram Voice Alerts, Google Sheets & ThingSpeak Cloud Dashboard

AI Smart Solar Panel Tracking System with Weather Optimization Agent AI-Powered ESP32 Agentic IoT Solar Tracker using n8n Automation, Telegram Voice Alerts, Google Sheets & ThingSpeak Cloud Dashboard 1. Project Overview This project automatically tracks the sun using a dual-axis solar panel tracker and uses AI-based weather optimization to maximize solar energy generation. The system uses: ESP32 WiFi Controller LDR Sensors for Sun Tracking Servo Motors for Panel Movement Weather Data Monitoring AI Agent Logic n8n Workflow Automation Telegram Voice Alerts Google Sheets Data Logging ThingSpeak Cloud Dashboard IoT Web Monitoring Page The AI Agent analyzes: Solar intensity Weather conditions Cloud coverage Battery status Power generation trends and automatically optimizes panel positioning. 2. Objectives Main Goals ✅ Maximize solar energy generation ✅ Reduce energy losses during cloudy conditions ✅ Real-time remote monitoring ✅ AI-based power prediction ✅ Telegram Voice Alerts ✅ Cloud Dashboard ✅ Automated Data Logging 3. System Architecture Sunlight ↓ LDR Sensors ↓ ESP32 ↓ Servo Motors ↓ Solar Panel Positioning ↓ Power Generation Data ↓ ThingSpeak Cloud ↓ n8n Workflow ↓ AI Agent Analysis ↓ Google Sheets Storage ↓ Telegram Alerts ↓ Voice Notification 4. Components Required Component Quantity ESP32 Dev Board 1 Solar Panel 6V 1 LDR Sensor 4 10K Resistors 4 SG90 Servo Motor 2 INA219 Current Sensor 1 DHT11 Sensor 1 16x2 LCD I2C 1 Breadboard 1 Jumper Wires Many Li-ion Battery 1 TP4056 Charger Module 1 Voltage Sensor Module 1 5. Working Principle Sun Tracking 4 LDRs are placed: LDR1 LDR2 LDR3 LDR4 ESP32 continuously compares sensor values. Example: LDR1 = 800 LDR2 = 600 Difference = 200 Panel rotates toward higher light intensity. Weather Optimization ESP32 collects: Temperature Humidity Solar Intensity AI Agent predicts: Sunny Partly Cloudy Cloudy Rainy and adjusts tracking strategy. 6. Circuit Connections LDR Connections LDR ESP32 Pin LDR1 GPIO34 LDR2 GPIO35 LDR3 GPIO32 LDR4 GPIO33 DHT11 DHT11 ESP32 DATA GPIO4 VCC 3.3V GND GND Servo Motors Horizontal Servo Signal → GPIO18 Vertical Servo Signal → GPIO19 INA219 INA219 ESP32 SDA GPIO21 SCL GPIO22 LCD LCD ESP32 SDA GPIO21 SCL GPIO22 7. Flowchart START ↓ Read LDR Values ↓ Compare Light Levels ↓ Move Servos ↓ Read DHT11 ↓ Read INA219 ↓ Calculate Power ↓ Upload to ThingSpeak ↓ Trigger n8n ↓ AI Analysis ↓ Store in Google Sheets ↓ Send Telegram Alert ↓ Repeat 8. ESP32 Source Code Required Libraries WiFi.h HTTPClient.h Servo.h DHT.h Wire.h Adafruit_INA219.h ThingSpeak.h WiFi Credentials const char* ssid="YOUR_WIFI"; const char* password="YOUR_PASSWORD"; ThingSpeak Setup unsigned long channelID = YOUR_CHANNEL_ID; const char* writeAPIKey = "YOUR_API_KEY"; Data Upload ThingSpeak.setField(1, temperature); ThingSpeak.setField(2, humidity); ThingSpeak.setField(3, voltage); ThingSpeak.setField(4, current); ThingSpeak.setField(5, power); ThingSpeak.writeFields(channelID, writeAPIKey); 9. ThingSpeak Dashboard Setup Create Account Go to: ThingSpeak Create Channel Fields: Temperature Humidity Voltage Current Power Solar Intensity Tracker Angle Dashboard Widgets Create: Gauge Line Chart Power Trend Graph Weather Prediction Graph 10. Google Sheets Integration Create Sheet: Date Time Temperature Humidity Voltage Current Power Weather Prediction Example: 31-05-2026 12:30 PM 34°C 58% 6.4V 0.95A 6.08W Sunny High Output 11. Telegram Bot Setup Create Bot Open: BotFather on Telegram Commands: /newbot Save: BOT TOKEN Get Chat ID Open: https://api.telegram.org/botTOKEN/getUpdates Copy Chat ID. 12. n8n Workflow Setup Install: n8n Official Website Workflow ThingSpeak Webhook ↓ Data Processing ↓ AI Agent ↓ Weather Prediction ↓ Google Sheets ↓ Telegram Message ↓ Telegram Voice Alert 13. AI Agent Logic Inputs: Temperature Humidity Solar Intensity Voltage Current Example Rules IF Solar > 800 AND Humidity < 60 Prediction: Sunny High Power Generation IF Solar < 300 AND Humidity > 80 Prediction: Cloudy/Rain Low Generation AI Output { "weather":"Sunny", "expected_power":"6.5W", "tracking_mode":"Normal", "confidence":"92%" } 14. Power Consumption Prediction Formula: P=V×I Example: Voltage = 6V Current = 1A Power = 6 Watts Daily Energy: E=P×t Example: 6W × 8 Hours = 48 Wh/day 15. Voice Notification Automation n8n converts AI response into voice. Example Alert: Attention. Solar tracker operating normally. Current Power Output: 6.2 Watts. Weather Prediction: Sunny. Battery Status: Charging Successfully. Telegram sends: 🎤 Voice Message 📱 Text Alert 16. Telegram Notifications Examples: High Generation ☀️ Solar Output High Power: 6.8W Weather: Sunny Efficiency: 95% Cloud Warning ☁️ Weather Alert Cloud Cover Detected Expected Power Drop: 35% Servo Failure Alert ⚠️ Tracker Motor Error Panel Movement Not Detected 17. IoT Web Dashboard Dashboard Cards: Live Values Temperature Humidity Voltage Current Power AI Section Weather Prediction Efficiency Score Energy Forecast Tracking Section Horizontal Angle Vertical Angle Sun Position Analytics Daily Energy Weekly Energy Monthly Energy 18. Future Enhancements AI Improvements Machine Learning Forecasting OpenWeatherMap API Integration Cloud Cover Detection Seasonal Learning Hardware Upgrades MPPT Solar Controller ESP32-CAM Cloud Detection GPS-based Sun Position Tracking Battery Health Monitoring Industry Features Remote Firmware Updates MQTT Cloud Integration AWS IoT Core Azure IoT Hub Predictive Maintenance 19. Expected Output Real-Time Monitoring ✔ Solar Tracking ✔ Weather Prediction ✔ Telegram Voice Alerts ✔ Google Sheets Logging ✔ ThingSpeak Dashboard ✔ AI Decision Making ✔ Power Forecasting ✔ Cloud Monitoring 20. Project Outcome This project combines Solar Energy + ESP32 + IoT + AI Agent + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Cloud Analytics into a complete smart renewable-energy platform. It demonstrates real-world concepts such as intelligent solar tracking, cloud-based monitoring, predictive analytics, automation workflows, and AI-driven decision making suitable for engineering final-year projects, IoT research, smart energy systems, and renewable energy applications.

AI Smart Solar Panel Tracking System with Weather Optimization

AI Smart Solar Panel Tracking System with Weather Optimization ESP32 + AI Agent + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Cloud Dashboard

AI Smart Solar Panel Tracking System with Weather Optimization ESP32 + AI Agent + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Cloud Dashboard 1. Project Overview Project Title AI-Powered Smart Solar Panel Tracking and Energy Optimization System Objective Develop an intelligent solar tracking system that: Tracks the sun automatically using ESP32. Adjusts panel position based on weather conditions. Predicts solar power generation using AI. Stores data in cloud platforms. Sends Telegram notifications and voice alerts. Maintains historical logs in Google Sheets. Provides a real-time dashboard through ThingSpeak. Uses n8n as the automation and AI orchestration platform. 2. System Architecture Sunlight Sensors │ ▼ ┌─────────────┐ │ ESP32 │ └──────┬──────┘ │ Sensor Data + GPS │ ▼ ThingSpeak Cloud │ ▼ n8n ┌────────┼─────────┐ ▼ ▼ ▼ Telegram AI Agent Google Sheet Alerts Analysis Data Logging │ ▼ Voice Notification 3. Features Smart Solar Tracking Dual-axis solar tracking Maximum sunlight capture Servo motor control Weather Optimization Rain detection Wind protection mode Cloud cover prediction AI Agent Predict power generation Detect abnormal conditions Recommend maintenance Notifications Telegram messages Telegram voice alerts Daily reports Cloud Monitoring ThingSpeak Dashboard Google Sheets Logging Historical analytics 4. Components List Controller Component Quantity ESP32 Dev Board 1 Sensors Sensor Purpose LDR Sensor x4 Sunlight direction DHT22 Temperature & Humidity Rain Sensor Rain detection INA219 Voltage & Current BH1750 Lux measurement Optional GPS NEO-6M Location Actuators Component Quantity MG996R Servo 2 Servo Driver PCA9685 1 Power Component Quantity Solar Panel 1 Li-ion Battery 1 TP4056 Charging Module 1 Buck Converter 1 Cloud & Software ESP32 Arduino IDE ThingSpeak Telegram Bot Google Sheets n8n OpenAI API Web Dashboard 5. Hardware Connections LDR Connections Four LDRs arranged as: LDR1 LDR2 Solar Panel LDR3 LDR4 ESP32 Pins Sensor ESP32 Pin LDR1 GPIO34 LDR2 GPIO35 LDR3 GPIO32 LDR4 GPIO33 Rain Sensor GPIO27 DHT22 GPIO4 Servo Horizontal GPIO18 Servo Vertical GPIO19 INA219 INA219 ESP32 SDA GPIO21 SCL GPIO22 BH1750 Shared I2C Bus: SDA → GPIO21 SCL → GPIO22 6. Circuit Schematic +----------------+ | Solar Panel | +-------+--------+ | INA219 | ▼ +--------------------------------+ | ESP32 | | | | GPIO34 ← LDR1 | | GPIO35 ← LDR2 | | GPIO32 ← LDR3 | | GPIO33 ← LDR4 | | GPIO27 ← Rain Sensor | | GPIO4 ← DHT22 | | GPIO18 → Servo X | | GPIO19 → Servo Y | +--------------------------------+ │ ▼ WiFi Network │ ▼ ThingSpeak │ ▼ n8n / | \ Telegram AI Google Sheet 7. Working Principle Step 1 LDR sensors detect sunlight intensity. Step 2 ESP32 compares: Left = LDR1 + LDR3 Right = LDR2 + LDR4 If: Left > Right Rotate left. Else rotate right. Step 3 Vertical Adjustment Top = LDR1 + LDR2 Bottom = LDR3 + LDR4 Move panel accordingly. Step 4 Measure: Temperature Humidity Solar voltage Solar current Lux level Step 5 Upload to ThingSpeak. Step 6 n8n fetches data. Step 7 AI Agent analyzes trends. Step 8 Notifications sent via Telegram. 8. Flowchart START | Initialize ESP32 | Read Sensors | Track Sun | Weather Check | Measure Power | Upload Cloud | Run AI Analysis | Send Alerts | Wait 60 sec | Repeat 9. ESP32 Source Code (Core Logic) #include #include #include Servo servoX; Servo servoY; int ldr1=34; int ldr2=35; int ldr3=32; int ldr4=33; int posX=90; int posY=90; void setup() { Serial.begin(115200); servoX.attach(18); servoY.attach(19); WiFi.begin("SSID","PASSWORD"); while(WiFi.status()!=WL_CONNECTED) { delay(500); } } void loop() { int a=analogRead(ldr1); int b=analogRead(ldr2); int c=analogRead(ldr3); int d=analogRead(ldr4); int left=a+c; int right=b+d; int top=a+b; int bottom=c+d; if(left-right>50) posX--; if(right-left>50) posX++; if(top-bottom>50) posY++; if(bottom-top>50) posY--; posX=constrain(posX,0,180); posY=constrain(posY,0,180); servoX.write(posX); servoY.write(posY); delay(1000); } 10. ThingSpeak Setup Create account: ThingSpeak Create Channel: Fields: Field Purpose Field1 Voltage Field2 Current Field3 Power Field4 Lux Field5 Temperature Field6 Humidity Field7 Rain Field8 Tracker Angle Get: Channel ID Write API Key Read API Key ESP32 uploads: ThingSpeak.writeField(channelID,1,voltage,key); 11. Google Sheets Integration Create Sheet Columns: Timestamp Voltage Current Power Lux Temperature Humidity Rain Prediction Status Google Apps Script function doPost(e) { var sheet = SpreadsheetApp.getActiveSpreadsheet() .getSheetByName("SolarData"); var data = JSON.parse(e.postData.contents); sheet.appendRow([ new Date(), data.voltage, data.current, data.power, data.lux, data.temp ]); return ContentService .createTextOutput("OK"); } Deploy as: Web App Anyone Access 12. Telegram Bot Setup Open Telegram. Search: BotFather Create Bot: /newbot Receive: BOT TOKEN Get Chat ID: https://api.telegram.org/botTOKEN/getUpdates Message API https://api.telegram.org/botTOKEN/sendMessage 13. Voice Notification Automation n8n workflow: Sensor Data | IF Condition | Generate TTS | Telegram Send Voice Examples: Warning. Rain detected. Solar panel moved to safe position. Voice generation options: OpenAI TTS Google TTS Edge TTS 14. AI Power Prediction Logic Input Features: Lux Temperature Humidity Time Weather Historical Power Simple Formula Predicted Power = 0.6 × Lux + 0.2 × Temp + 0.2 × Historical Average Advanced AI Model Use: Random Forest XGBoost LSTM Training Dataset: Date Lux Temp Humidity Current Voltage Power Output Prediction Output { "expected_power":145, "confidence":92 } 15. n8n Workflow Design Workflow Structure Schedule Trigger | ThingSpeak API | Function Node | OpenAI Agent | IF Node | ┌────┴─────┐ ▼ ▼ Telegram Google Sheet Alert Log Data AI Agent Prompt You are a solar energy monitoring assistant. Analyze: Voltage Current Power Temperature Humidity Rain Predict future power generation. Detect anomalies. Recommend actions. 16. Example n8n Workflow JSON Structure { "nodes":[ { "name":"Schedule Trigger" }, { "name":"ThingSpeak" }, { "name":"OpenAI" }, { "name":"Telegram" } ] } In n8n: Cron Node HTTP Request OpenAI Node IF Node Telegram Node Google Sheets Node 17. Weather Optimization Logic Rain Rain = TRUE Action: Tilt panel to 0° Strong Wind Action: Horizontal safe mode Cloudy Action: Optimize angle using AI prediction 18. Cloud Dashboard ThingSpeak Widgets Gauge Power Chart Voltage Chart Lux Graph Temperature Graph Tracker Position Dashboard shows: Live Solar Output Today's Energy Predicted Energy Weather Status Servo Angles 19. Future Enhancements Computer Vision Use: ESP32-CAM Sky image analysis Machine Learning Energy forecasting Cloud movement prediction Edge AI Run TinyML directly on ESP32. Digital Twin Virtual solar farm simulation. Predictive Maintenance Detect: Dust accumulation Servo failure Panel degradation 20. Deployment Guide Phase 1 Hardware Assembly Connect sensors Mount servos Install panel Phase 2 ESP32 Firmware Upload Configure WiFi Add API keys Upload code Phase 3 Cloud Configuration ThingSpeak channel Google Sheet Telegram Bot Phase 4 n8n Deployment You can self-host using: n8n Official Website or deploy on: Docker VPS Cloud VM Phase 5 AI Agent Integration Connect: ThingSpeak OpenAI API Telegram Google Sheets Final Outcome The completed system continuously: Tracks the sun using dual-axis control. Measures environmental and electrical parameters. Uploads telemetry to ThingSpeak. Logs data into Google Sheets. Uses an AI agent to predict energy production. Sends Telegram text and voice alerts. Optimizes panel positioning based on weather. Provides a real-time cloud dashboard with analytics and forecasting.

AI Smart Road Pothole Detection and Mapping System

AI Smart Road Pothole Detection and Mapping System AI-Powered ESP32 + Agentic IoT + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Cloud Dashboard

AI Smart Road Pothole Detection and Mapping System AI-Powered ESP32 + Agentic IoT + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Cloud Dashboard 1. Project Overview Project Title AI Smart Road Pothole Detection and Mapping System using ESP32, Agentic IoT, n8n Automation, Telegram Voice Alerts, Google Sheets, and ThingSpeak Cloud Dashboard Objective The objective of this project is to: Detect road potholes automatically using sensors connected to ESP32. Collect pothole location data using GPS. Send real-time data to cloud platforms. Store pothole records in Google Sheets. Display pothole statistics on ThingSpeak Dashboard. Trigger AI-based notifications through Telegram. Generate voice alerts using AI automation. Predict power consumption and battery health using AI logic. Create a scalable smart-city road monitoring solution. 2. System Architecture Road Pothole │ ▼ MPU6050 Accelerometer │ ▼ ESP32 │ ├────────► ThingSpeak Dashboard │ ├────────► n8n Webhook │ │ │ ▼ │ AI Decision Agent │ │ │ ┌────────┼─────────┐ │ ▼ ▼ │ Google Sheets Telegram Bot │ │ │ ▼ │ Voice Notification │ ▼ GPS Location Data 3. Working Principle The accelerometer continuously monitors road vibrations. When: Acceleration > Threshold The system identifies a pothole event. ESP32 then: Reads GPS coordinates. Measures vibration intensity. Calculates pothole severity. Uploads data to: ThingSpeak n8n Webhook n8n performs: AI classification Data logging Voice generation Telegram notification Google Sheet storage 4. Components List Component Quantity ESP32 Dev Board 1 MPU6050 Accelerometer & Gyroscope 1 NEO-6M GPS Module 1 SIM800L GSM Module (Optional) 1 Buzzer 1 LED Indicator 1 Li-Ion Battery 1 TP4056 Charging Module 1 Voltage Regulator 1 Jumper Wires As required Breadboard / PCB 1 5. Hardware Connections MPU6050 → ESP32 MPU6050 ESP32 VCC 3.3V GND GND SDA GPIO21 SCL GPIO22 GPS NEO-6M → ESP32 GPS ESP32 VCC 3.3V GND GND TX GPIO16 RX GPIO17 Buzzer Buzzer ESP32 + GPIO25 - GND LED LED ESP32 Anode GPIO26 Cathode GND 6. Circuit Schematic Diagram MPU6050 +----------+ | SDA SCL | +----|--|--+ | | | | GPIO21 GPIO22 ESP32 +-------------+ | | GPS TX -->| GPIO16 | GPS RX <--| GPIO17 | BUZZER -->| GPIO25 | LED ----->| GPIO26 | | | +-------------+ | | WiFi Internet | ▼ ThingSpeak + n8n 7. Flowchart START │ ▼ Initialize ESP32 │ ▼ Connect WiFi │ ▼ Read MPU6050 Data │ ▼ Acceleration > Threshold? │ ┌┴────────────┐ │ │ NO YES │ │ ▼ ▼ Continue Read GPS Monitoring │ ▼ Calculate Severity │ ▼ Send Data to Cloud │ ▼ Trigger n8n │ ▼ AI Agent Analysis │ ▼ Telegram Voice Alert │ ▼ Store Google Sheet │ ▼ END 8. Pothole Severity Classification Severity Acceleration Value Low 1.0g – 1.5g Medium 1.5g – 2.5g High > 2.5g 9. ESP32 Source Code #include #include #include #include MPU6050 mpu; const char* ssid = "YOUR_WIFI"; const char* password = "YOUR_PASSWORD"; String webhookURL = "https://your-n8n-server/webhook/pothole"; float threshold = 1.5; void setup() { Serial.begin(115200); WiFi.begin(ssid,password); while(WiFi.status()!=WL_CONNECTED) { delay(500); } Wire.begin(); mpu.initialize(); } void loop() { int16_t ax, ay, az; mpu.getAcceleration(&ax,&ay,&az); float vibration = sqrt(ax*ax+ay*ay+az*az)/16384.0; if(vibration > threshold) { sendData(vibration); } delay(1000); } void sendData(float value) { HTTPClient http; http.begin(webhookURL); http.addHeader("Content-Type", "application/json"); String payload = "{\"severity\":" + String(value) + "}"; http.POST(payload); http.end(); } 10. n8n Workflow Architecture Webhook │ ▼ AI Agent │ ├────► Google Sheets │ ├────► ThingSpeak Update │ ├────► OpenAI Analysis │ └────► Telegram Alert 11. n8n Workflow Steps Node 1: Webhook Method: POST Receive: { "severity": 2.8, "latitude": 17.3850, "longitude": 78.4867 } Node 2: AI Agent Prompt: Analyze pothole severity. If severity > 2.5 Category = Critical If severity > 1.5 Category = Medium Else Category = Low Node 3: Google Sheets Columns: Date Time Latitude Longitude Severity Category Status Node 4: Telegram Notification Message: ⚠️ Pothole Detected Location: 17.3850,78.4867 Severity: Critical Immediate inspection required. 12. Example n8n Workflow JSON { "nodes": [ { "name": "Webhook" }, { "name": "AI Agent" }, { "name": "Google Sheets" }, { "name": "Telegram" } ] } 13. Telegram Bot Setup Step 1 Open Telegram Search: @BotFather Create bot: /newbot Step 2 Copy Bot Token. Example: 123456:ABCDEF Step 3 Add token in n8n Telegram node. 14. Voice Notification Automation AI Voice Message Message generated: Warning. Critical pothole detected. Location latitude 17.3850 longitude 78.4867. Municipal inspection required. Workflow AI Agent │ ▼ Text to Speech │ ▼ MP3 File │ ▼ Telegram Send Audio 15. Google Sheets Integration Create Sheet: Pothole_Database Columns: Timestamp Latitude Longitude Severity Category Action Connect Google Account in n8n. Use: Append Row Node. 16. ThingSpeak Dashboard Setup Create channel on: ThingSpeak Fields: Field Purpose Field1 Severity Field2 Latitude Field3 Longitude Field4 Power Consumption Field5 Pothole Count Visualization Charts: Severity Trend GPS Heatmap Daily Pothole Count Power Usage Trend 17. AI Power Consumption Prediction Logic Inputs Battery Voltage WiFi Usage Sensor Sampling Rate GPS Activity Formula P=V×I Where: P = Power V = Voltage I = Current AI Rule Engine IF Battery < 20% Reduce Sampling Rate Disable GPS Continuous Mode Send Battery Alert Predicted States Battery Status >80% Healthy 50-80% Normal 20-50% Warning <20% Critical 18. AI Agent Decision Logic Input: Severity + Location + Historical Data AI Agent evaluates: 1. Repeated pothole? 2. High traffic area? 3. Severity level? 4. Repair priority? Priority Score Priority = (Severity × 50%) + (Traffic Density × 30%) + (Repeat Count × 20%) 19. ThingSpeak Data Format Example: field1=2.8 field2=17.3850 field3=78.4867 field4=1.2 field5=45 HTTP Request: https://api.thingspeak.com/update?api_key=YOURKEY&field1=2.8 20. Advanced Future Enhancements Computer Vision Pothole Detection Add: ESP32-CAM Edge AI Models: YOLOv8 Nano MobileNet SSD GIS Mapping Integrate: OpenStreetMap Google Maps API Display: Pothole clusters Maintenance zones Smart City Dashboard Features: Heatmaps AI Analytics Municipal Alerts Maintenance Scheduling Predictive Maintenance Use: Historical pothole data Rainfall data Traffic data Predict: Road Failure Probability before pothole formation. 21. Deployment Guide Phase 1: Prototype ESP32 MPU6050 GPS WiFi Phase 2: Pilot Install on: Municipal vehicles Buses Garbage trucks Phase 3: Smart City Scale Deploy: 100+ Nodes Central Cloud Dashboard AI Maintenance Management 22. Expected Outputs ✅ Real-time pothole detection ✅ GPS-based pothole mapping ✅ AI severity classification ✅ Telegram text alerts ✅ Telegram voice alerts ✅ Google Sheets logging ✅ ThingSpeak cloud visualization ✅ AI power management ✅ Smart-city ready deployment ✅ Fully scalable Agentic IoT architecture This architecture is suitable for final-year engineering projects, smart-city research, municipal road monitoring, and AIoT deployments with ESP32, n8n, Telegram automation, Google Sheets, and cloud analytics.

AI Smart Refrigerator Monitoring and Food Expiry Detection

AI Smart Refrigerator Monitoring & Food Expiry Detection System ESP32 + Agentic AI + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Cloud Dashboard

AI Smart Refrigerator Monitoring & Food Expiry Detection System ESP32 + Agentic AI + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Cloud Dashboard 1. Project Overview This project creates an intelligent refrigerator monitoring system that: ✅ Monitors refrigerator temperature and humidity ✅ Detects food expiry dates ✅ Predicts future power consumption using AI logic ✅ Stores data in Google Sheets ✅ Visualizes data in ThingSpeak Dashboard ✅ Sends Telegram alerts ✅ Generates Voice Notifications through Telegram ✅ Uses n8n automation as the workflow engine ✅ Uses ESP32 as the IoT edge device ✅ Can be extended into a fully Agentic AI Refrigerator Assistant 2. System Architecture +----------------+ | Refrigerator | +-------+--------+ | v +----------------+ | ESP32 | | DHT22 Sensor | | RFID/Manual | | Entry System | +-------+--------+ | WiFi MQTT/HTTP | v +----------------+ | ThingSpeak | | Cloud Dashboard| +-------+--------+ | v +----------------+ | n8n Workflow | +-------+--------+ | +----------------+ | | v v +--------------+ +--------------+ | Telegram Bot | | Google Sheet | +--------------+ +--------------+ | v +----------------+ | Voice Alerts | +----------------+ | v +----------------+ | AI Prediction | +----------------+ 3. Features Monitoring Refrigerator Temperature Humidity Door Open Duration Power Consumption Food Management Food Name Added Date Expiry Date Days Remaining Alerts High Temperature Food Expiry Power Consumption Anomaly Door Left Open Cloud Features Historical Data Dashboard Analytics AI Prediction 4. Hardware Components List Component Quantity ESP32 Dev Board 1 DHT22 Temperature Humidity Sensor 1 Reed Switch Door Sensor 1 RFID RC522 (optional) 1 RFID Tags 5 OLED Display 0.96" 1 Buzzer 1 Relay Module 1 ACS712 Current Sensor 1 Jumper Wires Several Breadboard 1 5V Adapter 1 5. Pin Connections DHT22 DHT22 ESP32 VCC 3.3V GND GND DATA GPIO4 Reed Switch Reed Switch ESP32 One Side GPIO15 Other Side GND Buzzer Buzzer ESP32 + GPIO18 - GND ACS712 Current Sensor ACS712 ESP32 OUT GPIO34 VCC 5V GND GND 6. Working Principle Step 1 ESP32 reads: Temperature Humidity Door Status Current Consumption every 30 seconds. Step 2 ESP32 sends data to: ThingSpeak n8n Webhook using HTTP requests. Step 3 n8n processes incoming data. Checks: Temperature > Threshold? Door Open Too Long? Power Consumption High? Food Expiry Near? Step 4 If abnormal: Telegram Message Telegram Voice Alert Google Sheets Entry generated automatically. 7. Flowchart START | v Initialize ESP32 | Connect WiFi | Read Sensors | Send to ThingSpeak | Send to n8n | Check Rules | +----No----+ | | | Continue | Yes | Send Telegram Alert | Generate Voice Alert | Store in Google Sheet | Repeat 8. ESP32 Source Code #include #include #include "DHT.h" #define DHTPIN 4 #define DHTTYPE DHT22 DHT dht(DHTPIN, DHTTYPE); const char* ssid = "YOUR_WIFI"; const char* password = "YOUR_PASSWORD"; String webhookURL = "https://your-n8n-domain/webhook/fridge"; String thingSpeakAPI = "YOUR_THINGSPEAK_WRITE_KEY"; void setup() { Serial.begin(115200); WiFi.begin(ssid,password); while(WiFi.status()!=WL_CONNECTED) { delay(1000); } dht.begin(); } void loop() { float temp = dht.readTemperature(); float hum = dht.readHumidity(); if(WiFi.status()==WL_CONNECTED) { HTTPClient http; String url = "https://api.thingspeak.com/update?api_key=" + thingSpeakAPI + "&field1=" + String(temp) + "&field2=" + String(hum); http.begin(url); http.GET(); http.end(); HTTPClient webhook; webhook.begin(webhookURL); webhook.addHeader( "Content-Type", "application/json"); String payload = "{\"temp\":" + String(temp) + ",\"humidity\":" + String(hum) + "}"; webhook.POST(payload); webhook.end(); } delay(30000); } 9. ThingSpeak Setup Create Account Create ThingSpeak account. Create new channel. Fields: Field1 Temperature Field2 Humidity Field3 Door Status Field4 Power Copy Write API Key Channels → API Keys → Write API Key Paste into ESP32 code. 10. Google Sheets Setup Create Sheet: Date Time Temperature Humidity Power Door Food Item Expiry Date Status Example: Date Temp Food Expiry 12-05-2026 4°C Milk 15-05-2026 11. Telegram Bot Setup Step 1 Open Telegram Search: BotFather Create Bot: / newbot Get: BOT TOKEN Step 2 Get Chat ID Open: https://api.telegram.org/botTOKEN/getUpdates Save Chat ID. 12. n8n Workflow Design Node 1 Webhook POST /fridge Node 2 IF Node Condition: {{$json.temp > 8}} Node 3 Telegram Node Message: ⚠ Refrigerator Temperature High Current: {{$json.temp}} °C Node 4 Google Sheets Node Append Row Date Time Temperature Humidity Node 5 Text-To-Speech Node Input: Warning. Refrigerator temperature is high. Please check immediately. Generate MP3. Node 6 Telegram Send Voice Attach generated MP3. 13. n8n Workflow JSON Structure { "nodes":[ { "name":"Webhook" }, { "name":"IF" }, { "name":"Telegram" }, { "name":"Google Sheets" } ] } Import and customize. 14. Food Expiry Detection Logic Google Sheet Example: Food Expiry Date Milk 15-May Eggs 20-May Yogurt 18-May n8n Daily Scheduler: Every Day 8AM Formula: daysRemaining = expiryDate - currentDate Conditions <=3 days Send Alert. Telegram: Milk expires in 2 days. 15. AI Food Expiry Prediction Advanced model considers: Temperature variation Humidity Storage duration Food category Door opening frequency Prediction: Expected Remaining Shelf Life Example: Milk Original: 7 days Predicted: 5 days because of frequent temperature spikes. 16. AI Power Consumption Prediction Input Features Temperature Compressor Runtime Door Open Count Humidity Historical Power Usage Model Linear Regression y = a + bx Where y = predicted power x = usage factors or Random Forest More accurate Training Dataset Date Power Temperature Door Count Prediction Output Tomorrow Expected Usage: 1.8 kWh 17. Voice Notification Automation Workflow: ESP32 ↓ n8n ↓ OpenAI/TTS Engine ↓ Generate Voice ↓ Telegram Voice Message Example Voice: Attention. Milk will expire in 2 days. Please consume it soon. 18. AI Agent Features The AI Agent can answer: What food expires today? How much power did fridge consume? Why is temperature rising? Suggest grocery items. Agent accesses: ThingSpeak Google Sheets Historical Records through APIs. 19. Future Enhancements Computer Vision ESP32-CAM Detect: Milk Eggs Fruits Vegetables using object detection. QR Code Inventory Each food item has QR code. Scan when inserted. Automatic inventory update. Voice Assistant Voice Commands: What expires today? How much milk is left? Mobile App Flutter App Features: Dashboard Notifications Analytics Inventory 20. Deployment Guide Local Testing Connect sensors. Upload ESP32 code. Verify serial monitor. Test ThingSpeak updates. Test n8n webhook. Cloud Deployment Deploy n8n on: Raspberry Pi Docker VPS Cloud VM Recommended: 2 CPU 4GB RAM Security Use: HTTPS Webhook Authentication Encrypted Tokens Firewall Rules 21. Expected Outputs Dashboard Temperature: 4.2°C Humidity: 68% Power: 1.5 kWh Door: Closed Telegram Alert ⚠ Warning Milk expires tomorrow. Voice Alert Attention. Milk expires tomorrow. AI Prediction Power tomorrow: 1.8 kWh Confidence: 92% 22. Project Outcome This system combines: ESP32 Edge Computing IoT Sensor Monitoring Agentic AI Decision Making n8n Workflow Automation Telegram Messaging & Voice Alerts Google Sheets Data Logging ThingSpeak Analytics Food Expiry Intelligence Predictive Maintenance The result is a complete Industry 4.0 smart refrigerator solution suitable for academic projects, final-year engineering projects, smart-home deployments, and IoT/AI portfolio demonstrations.

AI Smart Power Factor Correction with Load Prediction

AI Smart Power Factor Correction with Load Prediction ESP32 + Agentic AI + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Cloud Dashboard

AI Smart Power Factor Correction with Load Prediction ESP32 + Agentic AI + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Cloud Dashboard 1. Project Overview Project Title AI-Powered Smart Power Factor Correction System with Load Prediction using ESP32, n8n Automation, Telegram Voice Alerts, Google Sheets Logging, and ThingSpeak Cloud Dashboard Project Objective Develop an intelligent energy monitoring and power factor correction system that: Measures Voltage, Current, Power, Energy, and Power Factor. Automatically switches capacitor banks for power factor correction. Uses AI-based prediction to forecast future power consumption. Sends voice alerts through Telegram. Stores historical data in Google Sheets. Visualizes real-time data on ThingSpeak. Uses n8n as the automation and AI orchestration platform. Supports future Agentic AI decision-making. 2. System Architecture ┌────────────────────┐ │ Electrical Load │ └──────────┬─────────┘ │ Voltage & Current │ ┌─────────▼────────┐ │ PZEM004T │ │ Energy Meter │ └─────────┬────────┘ │ UART ┌─────────▼────────┐ │ ESP32 │ │ Data Collection │ └─────────┬────────┘ │ WiFi ┌──────────────────┼─────────────────┐ │ │ │ ▼ ▼ ▼ ThingSpeak n8n Workflow Google Sheets │ ▼ AI Prediction Engine │ ▼ Telegram Bot │ Voice Alerts ▼ Power Factor Control Relay Bank 3. Features Monitoring Voltage Current Active Power Apparent Power Reactive Power Power Factor Energy Consumption Automation Automatic capacitor switching AI load forecasting Telegram alerts Voice notifications Cloud ThingSpeak Dashboard Google Sheets Storage Historical Analytics AI Features Consumption Prediction Anomaly Detection Peak Demand Forecasting Future Agentic Actions 4. Components Required Component Quantity ESP32 Dev Board 1 PZEM-004T v3 Energy Meter 1 ZMPT101B Voltage Sensor (optional) 1 SCT013 Current Sensor (optional) 1 5V Relay Module 4 Capacitor Banks 4 Capacitors (2µF,4µF,8µF,16µF) As required Power Supply 5V 1 WiFi Router 1 Breadboard/PCB 1 Jumper Wires Multiple Telegram Bot 1 ThingSpeak Account 1 Google Account 1 n8n Server 1 5. Power Factor Correction Theory Power Factor: PF= Apparent Power Real Power ​ Ideal PF: 0.95 to 1.00 If PF drops: PF < 0.90 Capacitor bank is switched ON. Example: PF = 0.72 Relay 1 ON PF = 0.65 Relay 1 + Relay 2 ON PF = 0.55 Relay 1 + Relay 2 + Relay 3 ON 6. Circuit Connections ESP32 ↔ PZEM004T PZEM ESP32 TX GPIO16 RX GPIO17 VCC 5V GND GND Relay Module Relay ESP32 Relay1 GPIO25 Relay2 GPIO26 Relay3 GPIO27 Relay4 GPIO14 Capacitor Banks Relay1 → 2uF Relay2 → 4uF Relay3 → 8uF Relay4 →16uF Connected parallel to load. 7. Circuit Schematic AC LOAD │ ┌──▼──┐ │PZEM │ └──┬──┘ │ ▼ ESP32 │ ┌──┼───────────────┐ │ │ │ │ │ ▼ ▼ ▼ ▼ ▼ R1 R2 R3 R4 WiFi │ │ │ │ ▼ ▼ ▼ ▼ Capacitor Bank 8. Flowchart START │ ▼ Connect WiFi │ ▼ Read PZEM Data │ ▼ Calculate PF │ ▼ PF < 0.90 ? ┌──Yes──┐ ▼ ▼ Enable No Action Capacitor │ ▼ Send Data │ ▼ ThingSpeak │ ▼ n8n Webhook │ ▼ AI Prediction │ ▼ Store in Sheets │ ▼ Send Telegram Alert │ ▼ Repeat 9. ESP32 Source Code Libraries Install: PZEM004Tv30 WiFi HTTPClient ArduinoJson Main Code #include #include #include PZEM004Tv30 pzem(Serial2,16,17); const char* ssid="YOUR_WIFI"; const char* pass="PASSWORD"; String webhookURL = "https://n8n-server/webhook/power"; #define RELAY1 25 #define RELAY2 26 #define RELAY3 27 #define RELAY4 14 void setup() { Serial.begin(115200); pinMode(RELAY1,OUTPUT); pinMode(RELAY2,OUTPUT); pinMode(RELAY3,OUTPUT); pinMode(RELAY4,OUTPUT); WiFi.begin(ssid,pass); while(WiFi.status()!=WL_CONNECTED) { delay(500); } } void loop() { float voltage=pzem.voltage(); float current=pzem.current(); float power=pzem.power(); float pf=pzem.pf(); if(pf<0.90) { digitalWrite(RELAY1,HIGH); } if(pf<0.80) { digitalWrite(RELAY2,HIGH); } if(pf<0.70) { digitalWrite(RELAY3,HIGH); } if(pf<0.60) { digitalWrite(RELAY4,HIGH); } HTTPClient http; http.begin(webhookURL); http.addHeader("Content-Type", "application/json"); String payload="{\"voltage\":" +String(voltage)+ ",\"current\":" +String(current)+ ",\"power\":" +String(power)+ ",\"pf\":" +String(pf)+"}"; http.POST(payload); http.end(); delay(30000); } 10. ThingSpeak Setup Create channel. Fields: Field1 Voltage Field2 Current Field3 Power Field4 PF Field5 Energy Field6 Predicted Load Get: Write API Key Channel ID ESP32 sends data every 30 seconds. Example URL: https://api.thingspeak.com/update Parameters: api_key=XXXX field1=230 field2=5 field3=1100 field4=0.92 11. Google Sheets Integration Create Sheet: Timestamp Voltage Current Power PF Energy Prediction Status n8n Google Sheet Node Action: Append Row Every incoming ESP32 record gets stored. 12. Telegram Bot Setup Open Telegram. Search: BotFather Create bot: / newbot Receive: BOT TOKEN Get Chat ID. Save both. 13. Voice Alert System Telegram supports voice files. n8n workflow: Incoming Data │ ▼ Function Node │ ▼ Text-to-Speech │ ▼ Telegram Send Audio Example message: Warning. Power factor has dropped to 0.68 Capacitor bank activated. Predicted load increase within 30 minutes. 14. AI Load Prediction Logic Dataset Historical records: Time Voltage Current Power Energy PF Prediction Inputs Last 24 Hours Features: Hour Day Power Current Energy Prediction Output Next 30 min load Next 1 hour load Next 24 hour load Simple AI Model Linear Regression Predicted_Load = a+b(power)+c(current)+d(hour) Advanced AI Use: XGBoost Random Forest LSTM Prophet 15. n8n Workflow Design Webhook Trigger │ ▼ Data Processing │ ▼ AI Agent Node │ ├─────────► ThingSpeak │ ├─────────► Google Sheets │ ├─────────► Telegram Text │ └─────────► Telegram Voice 16. Example n8n Workflow JSON Structure { "nodes":[ { "name":"Webhook" }, { "name":"Function" }, { "name":"Google Sheets" }, { "name":"Telegram" } ] } In actual deployment export the workflow from n8n after configuration. 17. Agentic AI Extension AI Agent receives: PF Voltage Current Historical Trends Weather Time Agent decides: Increase Capacitor Decrease Capacitor Peak Warning Maintenance Alert Example: Predicted PF drop in 20 min Activate 8uF capacitor now. 18. Telegram Alert Examples Normal System Healthy PF = 0.97 Load = 1.1 kW Warning PF Low PF = 0.75 Capacitor Activated Critical PF = 0.52 Maximum Capacitor Bank Active Immediate inspection required 19. Future Enhancements AI LSTM Forecasting Reinforcement Learning Predictive Maintenance Load Classification Cloud MQTT Broker AWS IoT Azure IoT Hub Google Cloud IoT Hardware 3-Phase Monitoring Automatic Capacitor Bank Panel Industrial PLC Integration Mobile App Flutter Dashboard React Native Dashboard AI Chat Assistant 20. Deployment Guide Phase 1 Build hardware. Verify: Voltage readings Current readings PF readings Phase 2 Configure: WiFi ThingSpeak Telegram Phase 3 Deploy n8n. Recommended options: Docker VPS Raspberry Pi Phase 4 Connect: ESP32 → n8n n8n → Sheets n8n → Telegram n8n → ThingSpeak Phase 5 Train AI Model Collect: 1–4 weeks data Train prediction model and integrate it into n8n or a Python microservice. Final Outcome This project becomes a complete Industry 4.0 Smart Energy Management System capable of: Real-time electrical monitoring Automatic power factor correction AI-based load forecasting Agentic decision-making Cloud analytics Google Sheets logging ThingSpeak visualization Telegram text and voice alerts Scalable industrial deployment using ESP32 and n8n automation.

AI Smart Electric Vehicle Charging Station Management System

AI Smart Electric Vehicle Charging Station Management System AI-Powered ESP32 + Agentic IoT + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Cloud Dashboard

AI Smart Electric Vehicle Charging Station Management System AI-Powered ESP32 + Agentic IoT + n8n Automation + Telegram Voice Alerts + Google Sheets + ThingSpeak Cloud Dashboard 1. Project Overview Project Title AI Smart Electric Vehicle Charging Station Management System using ESP32, Agentic IoT, n8n Automation, Telegram Voice Alerts, Google Sheets, and ThingSpeak Objective Develop an intelligent EV charging station monitoring and management system that: Monitors charging voltage, current, power, and energy consumption. Predicts future power demand using AI. Sends real-time alerts through Telegram. Generates voice notifications automatically. Stores charging logs in Google Sheets. Visualizes data on ThingSpeak cloud dashboards. Uses n8n as an automation and AI orchestration platform. Supports future expansion into multiple charging stations. 2. System Architecture EV Charger │ ▼ Current & Voltage Sensors │ ▼ ESP32 Controller │ ├────────► ThingSpeak Dashboard │ ├────────► n8n Webhook │ │ │ ▼ │ AI Agent Logic │ │ │ ┌─────────┴─────────┐ │ ▼ ▼ │ Google Sheets Telegram Bot │ │ │ ▼ │ Voice Notification │ ▼ Cloud Monitoring 3. Features Real-Time Monitoring Voltage Monitoring Current Monitoring Power Calculation Energy Consumption Tracking AI Agent Features Charging load prediction Peak demand forecasting Anomaly detection Usage pattern analysis Automation Data logging Alert generation Voice message generation Cloud dashboard updates 4. Components List Component Quantity ESP32 Dev Board 1 ACS712 Current Sensor 1 ZMPT101B Voltage Sensor 1 Relay Module 1 OLED Display 0.96" 1 EV Charging Socket 1 5V Power Supply 1 Jumper Wires Several Breadboard/PCB 1 WiFi Router 1 5. Circuit Connections ACS712 Current Sensor ACS712 ESP32 VCC 5V GND GND OUT GPIO34 ZMPT101B Voltage Sensor ZMPT101B ESP32 VCC 5V GND GND OUT GPIO35 Relay Module Relay ESP32 IN GPIO26 VCC 5V GND GND OLED Display (I2C) OLED ESP32 SDA GPIO21 SCL GPIO22 VCC 3.3V GND GND 6. Circuit Schematic Diagram +----------------+ | ESP32 | | | Voltage Sensor--| GPIO35 | Current Sensor--| GPIO34 | Relay ----------| GPIO26 | OLED SDA -------| GPIO21 | OLED SCL -------| GPIO22 | +----------------+ | WiFi | +-------------+-------------+ | | ThingSpeak n8n Server | +----------------+----------------+ | | | Telegram Bot Google Sheets AI Agent 7. Flowchart Start │ ▼ Initialize ESP32 │ Connect WiFi │ Read Sensors │ Calculate Power │ Upload ThingSpeak │ Send Data to n8n │ AI Analysis │ Store in Sheets │ Alert Required? │ ┌──Yes───┐ ▼ ▼ Telegram Continue Voice Alert │ ▼ Loop 8. Working Principle Step 1 ESP32 reads: Voltage from ZMPT101B Current from ACS712 Step 2 Calculate power: P=V×I Example: Voltage = 230V Current = 10A Power = 230 × 10 = 2300 W Step 3 Calculate Energy E=P×t Example: 2300W × 2h = 4.6 kWh Step 4 ESP32 sends data to: ThingSpeak n8n Webhook Step 5 n8n processes data Save logs Trigger AI analysis Send notifications 9. ESP32 Source Code #include #include const char* ssid = "YOUR_WIFI"; const char* password = "YOUR_PASSWORD"; String webhookURL = "https://your-n8n-server/webhook/evstation"; int voltagePin = 35; int currentPin = 34; void setup() { Serial.begin(115200); WiFi.begin(ssid,password); while(WiFi.status()!=WL_CONNECTED) { delay(500); Serial.print("."); } } void loop() { float voltage = analogRead(voltagePin)*(3.3/4095.0)*100; float current = analogRead(currentPin)*(3.3/4095.0)*30; float power = voltage*current; if(WiFi.status()==WL_CONNECTED) { HTTPClient http; http.begin(webhookURL); http.addHeader("Content-Type", "application/json"); String payload="{"; payload+="\"voltage\":"+String(voltage)+","; payload+="\"current\":"+String(current)+","; payload+="\"power\":"+String(power); payload+="}"; http.POST(payload); http.end(); } delay(15000); } 10. ThingSpeak Setup Create Channel Sign up at ThingSpeak. Create New Channel. Add Fields: Field1 = Voltage Field2 = Current Field3 = Power Field4 = Energy Save Channel. Copy Write API Key. ESP32 Upload URL https://api.thingspeak.com/update Example: field1=230 field2=10 field3=2300 field4=4.5 11. Google Sheets Integration Create columns: Timestamp Voltage Current Power Energy Prediction Example: 2026-05-30 10:15 230 10 2300 4.6 2500 12. Telegram Bot Setup Create Bot Open Telegram. Search for: Telegram Open: BotFather Send: /newbot Enter Bot Name. Copy Token. Example: 123456:ABCDEFxxxx Get Chat ID Send message to your bot. Open: https://api.telegram.org/botTOKEN/getUpdates Copy Chat ID. 13. n8n Workflow Architecture Webhook │ ▼ Code Node │ ▼ AI Agent │ ┌─┴────────────┐ ▼ ▼ Google Sheet Telegram Alert 14. n8n Workflow Detailed Steps Node 1 Webhook Node POST /webhook/evstation Receives: { "voltage":230, "current":10, "power":2300 } Node 2 Function Node const power = $json.power; let status = "Normal"; if(power > 2500) { status = "High Load"; } return [{ json:{ power:power, status:status } }] Node 3 Google Sheets Node Append Row. Map: Timestamp Voltage Current Power Status Node 4 Telegram Node Message: ⚡ EV Charging Alert Power: {{$json.power}} Status: {{$json.status}} 15. n8n Workflow JSON { "name":"EV Station Workflow", "nodes":[ { "name":"Webhook" }, { "name":"AI Analysis" }, { "name":"Google Sheets" }, { "name":"Telegram" } ] } This is a simplified structure. In production, export the workflow directly from n8n after configuration. 16. AI Power Consumption Prediction Logic Dataset Stored in Google Sheets: Date Time Power Energy Temperature Prediction Features Current Power Historical Power Time of Day Charging Duration Simple Prediction Formula Moving Average: Prediction= 5 P 1 ​ +P 2 ​ +P 3 ​ +P 4 ​ +P 5 ​ ​ Example: 2200 2300 2400 2500 2600 Prediction: 2400W Advanced AI Use: Linear Regression Random Forest XGBoost LSTM Neural Networks via Python or AI APIs connected through n8n. 17. Voice Notification Automation Trigger Condition Power > 2500W n8n Flow IF Node │ ▼ Generate Speech │ ▼ Telegram Send Voice Voice Message Warning. Electric vehicle charging load has exceeded the safe limit. Current load is 2600 watts. 18. AI Agent Responsibilities The AI agent can: Monitor Power Voltage Current Energy Decide Overload detection Peak demand prediction Charger fault detection Act Notify user Log event Disable relay if dangerous 19. Automatic Relay Protection If Power > 3000W Relay OFF Send Alert Store Incident Pseudo-code: if(power > 3000) { digitalWrite(RELAY,LOW); } 20. Cloud Dashboard Design Dashboard Widgets Gauge 1 Voltage 0–250V Gauge 2 Current 0–32A Gauge 3 Power 0–7000W Chart Daily Consumption Chart Weekly Consumption 21. Database Structure ChargingLogs ------------ id timestamp voltage current power energy status prediction 22. Future Enhancements AI Features Dynamic charging optimization Peak tariff avoidance Smart load balancing Vehicle identification Battery health estimation IoT Features RFID authentication QR-code charging access Solar integration OCPP protocol support Multi-station management Mobile App Flutter dashboard Real-time monitoring Push notifications Usage analytics 23. Deployment Guide Local Deployment ESP32 connected to Wi-Fi n8n running on PC or Raspberry Pi Google Sheets cloud logging ThingSpeak dashboard active Cloud Deployment Deploy n8n on: n8n Cloud AWS Google Cloud Microsoft Azure 24. Expected Output Voltage : 228V Current : 11A Power : 2508W Energy : 5.2kWh AI Prediction: 2700W in next 30 minutes Status: High Load Action: Telegram Voice Alert Sent Google Sheet Updated ThingSpeak Updated 25. Project Outcome This project demonstrates a complete Industry 4.0 and Smart EV Infrastructure solution combining: ESP32 Edge Computing IoT Cloud Monitoring AI Agent Decision-Making n8n Workflow Automation Telegram Voice Notifications Google Sheets Analytics ThingSpeak Visualization Predictive Energy Management The architecture is scalable from a single charging point to a city-wide EV charging network with centralized AI monitoring and automated control.