Daily Averaged Wind Speed, Wind Direction, and Atmospheric Pressure from an IoT-Based Campus Weather Station
Description
This dataset contains daily averaged wind speed, wind direction, and atmospheric pressure measurements collected over a 31-day period using an IoT-based mini weather station deployed on a university campus in Nigeria. The data were generated to support campus-scale wind energy monitoring, system validation, and renewable energy feasibility studies. Measurements were obtained using low-cost sensors interfaced with an ESP8266 microcontroller and transmitted to a cloud-based repository.
Files
Steps to reproduce
1. Hardware Setup Assemble the IoT-based mini weather station using a microcontroller with integrated Wi-Fi ESP8266, a cup anemometer for wind speed measurement, an KY-040 rotary encoder for wind direction sensing, and a BMP280 barometric pressure sensor. Power the system using a regulated 5 V supply, with onboard voltage regulation providing 3.3 V for low-voltage sensors. 2. Sensor Configuration and Calibration Configure the anemometer to provide an analog voltage output proportional to wind speed (0–5 V). Condition this signal using a voltage divider to match the microcontroller ADC input range. Configure the KY-040 encoder in quadrature mode for wind direction measurement and apply a reference-direction calibration at installation. Configure the BMP280 sensor using standard oversampling and filtering settings recommended by the manufacturer. 3. Firmware Development and Deployment Develop firmware to read sensor outputs at regular intervals, convert raw ADC counts to engineering units (m/s, degrees, and kPa), and timestamp each measurement. Implement Wi-Fi connectivity in station mode and transmit the data to a cloud-based IoT platform (ThingSpeak) using HTTP requests or RESTful APIs. 4. Data Acquisition Deploy the weather station at an outdoor location free from obstructions, approximately at height of 5 meters above ground level. Operate the system continuously and log measurements over a 31-day period. Aggregate raw measurements into daily average values for wind speed, wind direction, and atmospheric pressure. 5. Data Export and Formatting Export the daily averaged data from the cloud platform in CSV format. Ensure the dataset includes timestamped entries with the following fields: Date, Wind Speed (m/s), Wind Direction (degrees), and Atmospheric Pressure (kPa). 6. Data Analysis (Optional Validation and Visualization) Use scientific computing tools such as Python to generate time-series plots, plot wind rose diagrams, and compute wind energy metrics (e.g., wind power density).