Smart Energy Meter using ESP-32 (IOT)

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1. Abstract

The IoT-Based Smart Energy Meter is an intelligent energy monitoring system designed to measure and track voltage, current, power consumption, and cost estimation for multiple household appliances. The system utilizes an ESP32 microcontroller connected to current and voltage sensorsto obtain real-time energy data. This data is processed, displayed and sent to an IoT dashboard and stored for historical analysis.

The project incorporates relays and for remote appliance control while ensuring electrical safety. A Google Sheets webbook is integrated to log energy data for further analysis. Machine Learning algorithms can be applied to predict usage patterns and optimize energy consumption.

Key challenges such as WiFi disconnection, inaccurate sensor readings, and overheating are mitigated through automatic reconnection mechanisms, software filtering techniques, and efficient PCB design with heat management solutions. Future enhancements include AI-based load prediction, solar energy integration, and voice control via Google Assistant/Alexa.

This system provides an efficient, scalable, and cost-effective solution for home energy management, allowing users to monitor, control, and optimize energy consumption in real-time.

2. Introduction

The rapid growth of urbanization and industrialization has led to an exponential increase in global electricity consumption, necessitating efficient and intelligent energy monitoring systems. Traditional energy meters provide only aggregate consumption data, lacking real-time insights, remote accessibility, or intelligent load management features. This limitation hinders consumers' ability to analyze, optimize, and control their power usage effectively.

An energy meter is a device used to measure the electrical energy consumed by a residence, business, or an electrically powered device. In modern smart grid infrastructures, energy meters have evolved into digital, network-enabled systems capable of capturing real-time data, communicating with cloud platforms, and enabling bidirectional energy flow in distributed energy networks.

3. Problem Statement

Several existing systems use Arduino and GSM for single-load energy monitoring. However, most lack cloud integration or multi-load capability. This project aims to overcome those limitations by using:

• ESP32 for multi-load monitoring with Wi-Fi capability.
• Google Sheets integration for cloud-based data logging.
• Blynk IoT platform for mobile-based visualization.

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4. Components used

I. SCT-013-030 (current sensor) x2

The SCT-013 is a Non-invasive AC Current Sensor Split Core Type Clamp Meter Sensor that can be used to measure AC current up to 30 amperes. Current transformers (CTs) are sensors are for measuring alternating current. They are particularly useful for measuring whole building electricity consumption. The SCT-013 current sensors can be clipped straight either to the live or neutral wire without having to do any high voltage electrical work.

Like any other transformer, a current transformer has a primary winding, a magnetic core, and a secondary winding. The secondary winding comprises many turns of fine wire housed within the casing of the transformer.

Specifications :

• Input Current: 0-30A AC
• Output Signal: DC 0-1 V
• Non-linearity: 2-3 %
• Built-in resistance (RL): 60 Ω
• Turn Ratio: 2000:1
• Resistance Grade: Grade B
• Work Temperature: -25 °C ~ +70 °C
• Dielectric Strength (between shell and output): 1000 V AC / 1 min 5 mA

II. ZMPT101b (voltage sensor) x1

The ZMPT101B AC Single Phase voltage sensor module is based on a high precision ZMPT101B voltage Transformer used to measure the accurate AC voltage with a voltage transformer. This is an ideal choice to measure the AC voltage using Arduino or ESP32.

The Modules can measure voltage within 250V AC voltage & the corresponding analog output can be adjusted. The module is simple to use and comes with a multi-turn trim potentiometer for adjusting and calibrating the ADC output.

Specifications

• Voltage up to 250 volts can be measured
• Lightweight with on-board micro-precision voltage transformer
• High precision on-board op-amp circuit
• Operating temperature : 40ºC ~ + 70ºC
• Supply voltage 5 volts to 30 volts

III. Two Channel Relay Module x1

Specifications

• Low Level Trigger Relay Module
• Two separate LEDs for On/Off indication of the Relay.
• Triggering input voltage 3.3V – 5V.
• Back EMF protection
• Opto isolation circuitry
• Module with diode current protection, short response time
• AC Control Voltage: 250V @max.10A
• DC Control Voltage: 30V @max. 10A

IV. ESP-WROOM-32 x1

The ESP-WROOM-32 is a powerful Wi-Fi + Bluetooth combo module developed by Espressif, and it's the core module in many ESP32-based development boards. Here's a quick breakdown of its key features and why it's popular in IoT and embedded systems projects:

Key Features :

• Processor: Dual-core Tensilica LX6 microprocessor, up to 240 MHz
• Memory: 520 KB SRAM
  4 MB Flash (typically, depending on the breakout board)
• Wireless: Wi-Fi: 802.11 b/g/n.
  Bluetooth: v4.2 BR/EDR and BLE
• GPIO Pins: 34 programmable GPIOs (input/output, PWM, ADC, DAC, I2C, SPI, UART, etc.)
• ADC: 12-bit, 18 channels (some shared with other functions)
• DAC: 2 channels (8-bit)
• Touch Sensors: 10 capacitive touch inputs
• PWM: Supported on all GPIOs
• Security: Secure boot, flash encryption, and cryptographic hardware acceleration
• Operating Voltage: 3.0V to 3.3V
• Power Consumption: Ultra-low power modes supported

V. OLED display x1 (SSD1306 0.96 inch )

An OLED (organic light-emitting diode) is used frequently in displaying texts, bitmap images, shapes, and different types of clocks. They offer good view angles and pixel density in a cost-effective manner.

Specifications

• Size : 0.96 inch
• Terminals : 4
• Pixels : 128×64
• Communication : I2C
• VCC : 3.3V-5V
• Operating Temperature : -40℃ to +80℃

VI. Capacitor(10µF) x2

VII. Buck Converter x1

A buck converter is a type of DC-DC converter that steps down voltage from its input (supply) to its output (load). It’s widely used in power electronics to efficiently supply lower voltages from higher ones, like converting 12V to 5V in embedded systems.

Specification

• Input voltage: 3-40V
• Output voltage: 1.5-35V(Adjustable)
• Output current: Rated current is 2A, maximum 3A
• Switching Frequency: 150KHz
• Operating temperature:Industrial grade (-40 to +85 )
• Conversion efficiency: 92%(highest)

VIII. Vero board x1

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5. Software Used

• Arduino IDE : The Arduino IDE (Integrated Development Environment) is a software platform used to write, compile, and upload code to Arduino boards. It's the main tool used to program Arduino-based microcontrollers.

• Circuit Designer : It is a tool that let you create, simulate, and share electronic circuits right from your browser, without needing to install anything.

• Blynk IOT: Blynk IoT is a popular platform for building IoT (Internet of Things) applications that allows you to control and monitor hardware devices (like ESP32, ESP8266, Arduino, etc.) from your smartphone or web dashboard — without needing to build your own backend infrastructure.

6. Block Diagram

This is a block diagram of a dual-load IoT-based energy monitoring and control system using an ESP32 microcontroller, integrating Blynk IoT, CT sensors, voltage transformer, relays, and display. Here's a breakdown of each block:

🔌 1. Power Supply (Left Side):
Provides the AC power to both LOAD1 and LOAD2.
This AC supply is monitored for current and voltage.

🔋 2. LOAD1 and LOAD2:
Two different electrical loads (e.g., lights, fans, appliances) being powered and monitored.
Relays are used to control (ON/OFF) these loads through the ESP32.

🧲 3. CT1 and CT2 (Current Transformers):

• CT1 monitors the current drawn by LOAD1.
• CT2 monitors the current drawn by LOAD2.
  These sensors measure current and send analog signals to the ESP32 for processing.

⚡ 4. VT (Voltage Transformer):
Measures the line voltage.
Sends a scaled-down analog signal to the ESP32 for safe voltage sensing.

🧠 5. ESP32 Microcontroller:
Core processing unit of the system.
Tasks:

• Reads current from CT1 and CT2.
• Reads voltage from VT.
• Calculates power consumption.
• Controls the relays (thus, LOAD1 and LOAD2).
• Sends data to the Blynk Cloud via Wi-Fi.
• Displays real-time values on a connected display (likely OLED or LCD).

📟 6. Display:
Shows local real-time data such as:

• Voltage
• Current
• Power usage for each load
• Possibly cost, energy consumed, etc.

🌐 7. Blynk IoT System (Cloud + Mobile App):
The ESP32 is connected to the Blynk cloud over Wi-Fi. A smartphone app communicates with Blynk cloud.
You can:

• Monitor real-time values (current, voltage, power).
• Control LOAD1 and LOAD2 using switches in the app.

7. Circuit Diagram

Component----ESP32
Current_1---- 32
Current_2---- 33
Relay_1---- 25
Relay_2---- 26
Voltage---- 34 & 35
OLED Display
Vcc------- 3.3V
GND------- GND
SCK------- 22
SDA------- 21

Complete Circuit Diagram

8. PCB design:

9. Algorithm:

Objective: Monitor voltage, current, power, energy consumption, and cost for two electrical loads using an ESP32. Control relays based on power thresholds, display data on an OLED screen, send data to Blynk for remote monitoring, and log data to Google Sheets.

✅ 1. Initialization Phase (setup())

• 1.1. Start Serial Communication
  • Begin Serial Monitor at baud rate 115200 for debugging.
• 1.2. Set Analog Resolution
  • Set ADC resolution to 10 bits using analogReadResolution(10);, suitable for reading analog sensor signals.
• 1.3. Connect to Wi-Fi
  • Start Wi-Fi using the provided SSID and password.
  • Keep trying until the connection is established.
• 1.4. Start Blynk Connection
  • Connect to Blynk using the template ID and auth token.
• 1.5. (Optional) Initialize OLED Display
  • OLED initialization code is present but commented out.
  • If enabled, it displays a welcome message.
• 1.6. Configure Relay Pins
  • Set relay1 and relay2 as outputs.
• Initialize them to LOW (off) state.
• 1.7. Initialize Energy Monitors
  • emon1 and emon2 are set up to monitor:
    • Voltage using ZMPT101B with a calibration constant of 520.
    • Current using SCT-013-000 with calibration constants:
      • 25 for Load 1
      • 95 for Load 2

🔁 2. Continuous Monitoring Loop (loop())

• 2.1. Run Blynk
  • Ensures real-time communication with the Blynk app.
  • 2.2. Sample Voltage & Current
  • Call calcVI(20, 2000) for both emon1 and emon2:
  • Measures 20 half-cycles (~1 second at 50Hz).
  • Timeout after 2000 ms.
• 2.3. Extract RMS & Power Values
  • Read voltage, current, real power, and apparent power.
  • Use fabs() to take absolute values and filter out negative noise.
• 2.4. Energy & Cost Calculation (every 10 seconds)
  • Check if 10 seconds have passed using millis().
  • If so:
    • Energy (kWh) = Power (W) × Time (hr)
      (realPower × interval / 3600000.0) / 1000
    • Cost = Energy × ₹10/kWh
    • Send data to Google Sheets via a URL with query parameters.
• 2.5. Relay Control
  • Turn ON relay if real power > 100W, else turn OFF.
• 2.6. Send Data to Blynk App
  • Push the following to Blynk virtual pins:
  • Voltage, Current, Energy, Power Factor, Cost, Relay State (for both loads)
• 2.7. OLED Display
  • Code for OLED is available but commented out.
  • Alternates display between Load 1 and Load 2 every 2 seconds.

🌐 3. Google Sheets Logging (sendToGoogleSheets())

• 3.1. Construct URL
  • Build a GET request URL with all sensor readings and calculations.
• 3.2. Send HTTP GET Request
  • Send data using http.GET() to a Google Apps Script Web App.
  • Print success/failure status to Serial Monitor.

📦 Summary of Main Components Used

Component	Purpose
EmonLib	For calculating voltage, current, and power.
WiFi + Blynk	Real-time monitoring on mobile app.
Google Sheets	Cloud-based logging for history/tracking.
Relays	Automatic load control based on power usage.
OLED (optional)	Local display for power/energy info.

⚙️ Current Caliberation Algorithm (SCT-013-030)

This section explains how the RMS current is calculated from the analog signal of the SCT-013-000 current sensor.

🔄 Algorithm Steps: 1. Define Constants:

• sensorPin is set to A0, where the SCT-013-000 sensor is connected.
• calibration factor is set to 30.0, based on the burden resistor and ADC scaling.
• VREF is set to 5.0V, the Arduino's reference voltage.

2. Initialize Serial Communication:

• Start Serial Monitor at 115200 baud for debugging and data plotting.

3. Sampling Loop:

• Take 200 samples in each loop cycle for a smooth RMS calculation.
• For each sample:
  • Read analog value from the sensor pin using analogRead(sensorPin).
  • Convert the raw value to voltage using:
    voltage = (sensorValue / 1023.0) * VREF
  • Convert voltage to instantaneous current using:
    current = (voltage - VREF/2) * calibration
  • Square the current and add to a running sum:
    sum += current * current
  • Print the instantaneous current value to the Serial Plotter.

4. Calculate RMS Current:

• After 200 samples, calculate RMS current as:
  rmsCurrent = sqrt(sum / sampleSize)
• Print the RMS current to the Serial Monitor.

5. Delay:

• Wait 1 second before repeating the loop to allow stable output.

📌 Notes:

• This code assumes the sensor outputs a centered AC signal around VREF/2.
• Adjust the calibration constant if using a different burden resistor or ADC resolution.

⚡ Voltage Caliberation Algorithm (ZMPT101B)**

This section describes how RMS voltage is measured using the ZMPT101B voltage sensor and an Arduino.

🔄 Algorithm Steps: 1. Define Constants:

• ZMPT101B_PIN is set to A0, where the voltage sensor is connected.
• calibrationFactor is set to 687, used to convert the measured RMS voltage to the actual AC voltage.
• sampleCount is set to 1000, for higher measurement accuracy.
• offsetVoltage is set to 2.5V, assuming a centered AC waveform from the sensor.

2. Initialize Serial Communication:

• Start Serial Monitor at 115200 baud to view voltage readings and debug output.

3. Sampling Loop:

• Initialize squaredSum to store the sum of squared voltages.
• For each of the 1000 samples:
  • Read the raw analog value from the ZMPT101B sensor:
    sensorValue = analogRead(ZMPT101B_PIN)
  • Convert the raw ADC value to voltage using:
    voltage = sensorValue * (5.0 / 1023.0)
  • Subtract the offset voltage (2.5V) to center the waveform around 0V.
  • Square the corrected voltage and add it to squaredSum.
  • Optionally print each corrected voltage value for visualization in the Serial Plotter.
  • Delay for 1000 microseconds between samples for adequate resolution.

4. Calculate RMS Voltage:

• After sampling, compute RMS voltage using:
  rmsVoltage = sqrt(squaredSum / sampleCount)

5. Output Results:

• Print the RMS voltage.
• Multiply the RMS voltage by the calibration factor to get the final voltage reading.
• Display both values on the Serial Monitor.

6. Delay:

• Add a small delay (500 ms) before the next reading cycle.

📌 Notes:

• The offset voltage is crucial for removing the DC bias from the ZMPT101B output.
• The calibration factor should be adjusted based on known voltage measurements for better accuracy.
• Sampling delay and count can be fine-tuned depending on application needs (e.g., 50Hz or 60Hz mains).

10. Flowchart

🧭 System Workflow Overview (Flowchart Explanation)

The following section describes the overall flow of the ESP32-based Smart Energy Monitoring and Control system. This flow is visualized in the project flowchart.

🔄 Process Breakdown:

🔹 1. Start & Initialization

• Initialize ESP32: Begin the system setup.
• Setup Wi-Fi: Connect to a predefined Wi-Fi network to enable Blynk and Google Sheets communication.
• Initialize OLED Display: Prepare the OLED screen (optional) for real-time data visualization.
• Set Relays OFF & Initialize Sensors: Both relay pins are set to OFF initially. Voltage and current sensors (ZMPT101B & SCT-013-000) are configured.

🔹 2. Data Acquisition

• Calculate Voltage & Current: Using sensor data, compute voltage (V), current (I), power values, and other metrics.
• Display Real-Time Data on OLED: Sensor values such as voltage, current, power, and energy are updated on the OLED in real time (if enabled).

🔹 3. Data Logging

• Push to Blynk Database: Live values are sent to the Blynk dashboard for mobile monitoring.
• Push to Google Sheets: Data is also logged to a Google Sheet via a Google Apps Script Web App for historical analysis.

🔹 4. Load Control Logic

• Check Load 1 Power:
  • If Load 1 power > 200W, turn Relay 1 ON.
  • Otherwise, keep Relay 1 OFF.
• Check Load 2 Power:
  • If Load 2 power > 1500W, turn Relay 2 ON.
  • Otherwise, turn Relay 2 OFF.

🔹 5. Loop Timing

• A 10-second delay is introduced before the next iteration of measurement and control logic begins.

✅ Summary: This workflow ensures:

• Continuous monitoring of two electrical loads.
• Automated control of connected relays based on power thresholds.
• Real-time visualization via OLED and Blynk app.
• Long-term logging using Google Sheets for analysis or billing.

📌 This smart energy system is ideal for home automation, load management, and energy consumption tracking applications.

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11. Result Verification

• voltage calibration

  

• 100W bulb as a low load

  

• 1350W electrickettle as a high load

  

• SCT current sensor installation

  

• Complete project installation

  

• BLYNK App Dashboard

  

• Google sheet view for data logging

  

12. System Verification Results

The table below summarizes the expected vs. observed results for key performance metrics of the Smart Energy Monitoring System.

Parameter	Expected Result	Observed Result
Current Reading	Accurate (within ±2%)	✓ Within tolerance
Voltage Reading	Measured (within ±2%)	✓ Within tolerance
Real Power	Calculated by EmonLib	✓ Matches approx usage
Energy Consumption	Cumulative kWh	✓ Calculated
Data to Blynk	Real-time display & control	✓ Successful
Data to Google Sheets	HTTP data logging	✓ Successful logging
Relay Function	Auto/manual ON/OFF	✓ Verified

This verification confirms that the system performs reliably across all major functional areas including sensing, control, real-time display, and cloud logging.

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12. Conclusion

The Smart Energy Meter system, developed using the ESP32 microcontroller, SCT-013 current sensors, EmonLib library, OLED display, Blynk IoT platform, and Google Sheets integration, offers a reliable and cost-effective solution for real-time energy monitoring.

By enabling dual-load measurement, users can monitor the power consumption, energy usage, and cost of two electrical loads simultaneously with high accuracy. The system’s integration with IoT platforms like Blynk and Google Sheets provides:

• 📱 Remote access to energy data
• 🔄 Real-time updates
• 📊 Historical data tracking

Additionally, the inclusion of relay-based control supports automation and active load management, thereby enhancing energy efficiency.

This project empowers users with valuable insights into their electricity usage and serves as a foundation for intelligent energy management systems. Future extensions may include machine learning integration for:

• 🔍 Anomaly detection
• 📈 Predictive analytics
• ⚙️ Dynamic load optimization

In conclusion, the project demonstrates how affordable hardware and open-source tools can be effectively combined to create a practical, scalable smart energy solution with real-world relevance, especially in smart homes and sustainable environments.

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A special thanks to :

1. Mr. Smruti Ranjan rout GitHub
2. Mr. Sumit Singh GitHub
3. Mr. Priyanshu Sinha GitHub