DIY IoT Weather Station with ESP32 and BME280: Complete FYP Build Guide

Why an IoT Weather Station Makes a Solid FYP

A weather station is one of the most beginner-friendly IoT builds, but that doesn't mean it can't score well — the difference between a pass and a distinction is entirely in what you build around the sensor. Raw readings are easy; forecasting trends, historical graphs, and alert thresholds are where the technical marks come from.

It's also a project that's easy to demo live: plug it in during your viva, and the panel sees real numbers updating in real time on a dashboard.

Hardware Components

Component	Purpose	Cost (RM)
ESP32 Development Board	Microcontroller + WiFi	RM 20–30
BME280 Sensor	Temperature, humidity, pressure	RM 12–18
Rain Sensor Module (optional)	Detect rainfall	RM 5–8
Anemometer (optional)	Wind speed	RM 30–50
OLED Display 0.96"	Local readout	RM 10–15
Weatherproof Enclosure	Outdoor housing	RM 15–25

Total hardware cost (basic build): RM 60–90

How It Works

1. The BME280 reads temperature, humidity, and barometric pressure over I2C every few seconds
2. The ESP32 pushes these readings to a cloud dashboard (Blynk, ThingSpeak, or a custom Firebase-backed web app)
3. Barometric pressure trends over a few hours can be used to make a basic "rain likely" prediction — a falling pressure trend generally signals incoming rain
4. Data is displayed locally on an OLED and logged for historical graphs
5. Optional: add a rain sensor and anemometer for a more complete station

Wiring Overview

BME280 (I2C):

• VCC → 3.3V
• GND → GND
• SDA → GPIO 21
• SCL → GPIO 22

OLED (shares the same I2C bus, different address):

• SDA → GPIO 21
• SCL → GPIO 22

Rain sensor (if used):

• Analog output → GPIO 34 (ADC pin)

Core Logic (Pseudocode)

void loop() {
  float temp = bme.readTemperature();
  float humidity = bme.readHumidity();
  float pressure = bme.readPressure() / 100.0F; // hPa

  logPressureHistory(pressure);
  String forecast = predictWeather(pressureHistory);

  displayOnOLED(temp, humidity, pressure, forecast);
  pushToDashboard(temp, humidity, pressure, forecast);

  delay(5000);
}

String predictWeather(float history[]) {
  float trend = history[latest] - history[3HoursAgo];
  if (trend < -1.5) return "Rain likely";
  if (trend > 1.5) return "Clearing up";
  return "Stable";
}

The pressure-trend forecasting logic above is simple but genuinely useful — it's the kind of "light algorithm" that gives examiners something to ask about beyond just wiring.

Dashboard Options

• ThingSpeak — free, built-in charting, good for showing weeks of historical data with minimal setup
• Blynk — best for a clean live gauge view during your demo
• Custom Firebase + web app — more effort, but lets you build a full dashboard with daily/weekly summaries, which is worth extra marks at degree level

Presentation Tips

• Show a graph of at least a week of logged data — a single live reading isn't enough to demonstrate the "IoT" part convincingly
• Be ready to explain how barometric pressure relates to weather changes; it's the most common question this project draws
• If you added the rain-prediction logic, walk the panel through your threshold choices and how you validated them against actual weather

Scope Recommendations

For a Diploma FYP: Live readings + basic dashboard For a Degree FYP: Add historical logging + rain-likelihood prediction For Merit/Distinction: Add anemometer, rain sensor, and a mobile-friendly dashboard with weekly reports

Need This Project Done?

Rectronx Circuits builds complete IoT weather stations — sensor calibration, dashboard setup, and full FYP documentation included. WhatsApp us for a free quote within 2 hours.

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