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<p>Reliable, site-specific wind data are essential for accurate wind energy assessment at campus and community scales, where conventional meteorological stations may be limited or economically expensive. This study presents the design, deployment, and experimental validation of a low-cost Internet of Things-based mini weather station (IoT-MWS) for wind monitoring at Edo State University, Iyamho (ESUI), Nigeria. The system integrates wind speed (WS02), wind direction (KY-040), and atmospheric pressure (BMP280) sensors with an ESP8266-based data acquisition unit, signal conditioning circuitry, wireless transmission, and cloud-based logging. Structured calibration modelling ensured traceable conversion from analogue signals to digital outputs. The anemometer exhibited a verified sensitivity of 0.1667&#xa0;V/(m/s), achieving a wind speed resolution of 0.029&#xa0;m/s per ADC least significant bit with ± 0.0145&#xa0;m/s quantization error, while signal scaling (α = 0.180) limited ADC input to 0.90&#xa0;V within the 1.0&#xa0;V reference (10-bit, Δ = 0.00088&#xa0;V/LSB). Wind direction resolution was 4.5° (± 2.25° maximum error), and pressure resolution was 0.002&#xa0;hPa with ± 0.001&#xa0;hPa quantization uncertainty, well below the ± 0.12&#xa0;hPa specified accuracy. Statistical analysis of measured wind data (N = 31) yielded a mean wind speed of 2.20&#xa0;m/s, standard deviation of 0.53&#xa0;m/s, and turbulence intensity of 24.1%, with northerly winds accounting for approximately 58% of occurrences. Wind power density analysis indicates low energy potential, unsuitable for utility-scale deployment but appropriate for small-scale and educational applications. The total system cost was $99.12, representing a 42% reduction relative to a benchmark academic prototype and 84–95% savings compared with commercial systems, demonstrating that reliable, statistically validated, and economically scalable campus-scale wind monitoring is achievable using low-cost IoT technologies.</p>

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Design and deployment of an IoT-based mini weather station network for campus-scale wind energy monitoring

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月25日

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  • Chukwuemeka C. Obasi,
  • Anita Iyeli Iruoghene,
  • Braimoh A. Ikharo

摘要

Reliable, site-specific wind data are essential for accurate wind energy assessment at campus and community scales, where conventional meteorological stations may be limited or economically expensive. This study presents the design, deployment, and experimental validation of a low-cost Internet of Things-based mini weather station (IoT-MWS) for wind monitoring at Edo State University, Iyamho (ESUI), Nigeria. The system integrates wind speed (WS02), wind direction (KY-040), and atmospheric pressure (BMP280) sensors with an ESP8266-based data acquisition unit, signal conditioning circuitry, wireless transmission, and cloud-based logging. Structured calibration modelling ensured traceable conversion from analogue signals to digital outputs. The anemometer exhibited a verified sensitivity of 0.1667 V/(m/s), achieving a wind speed resolution of 0.029 m/s per ADC least significant bit with ± 0.0145 m/s quantization error, while signal scaling (α = 0.180) limited ADC input to 0.90 V within the 1.0 V reference (10-bit, Δ = 0.00088 V/LSB). Wind direction resolution was 4.5° (± 2.25° maximum error), and pressure resolution was 0.002 hPa with ± 0.001 hPa quantization uncertainty, well below the ± 0.12 hPa specified accuracy. Statistical analysis of measured wind data (N = 31) yielded a mean wind speed of 2.20 m/s, standard deviation of 0.53 m/s, and turbulence intensity of 24.1%, with northerly winds accounting for approximately 58% of occurrences. Wind power density analysis indicates low energy potential, unsuitable for utility-scale deployment but appropriate for small-scale and educational applications. The total system cost was $99.12, representing a 42% reduction relative to a benchmark academic prototype and 84–95% savings compared with commercial systems, demonstrating that reliable, statistically validated, and economically scalable campus-scale wind monitoring is achievable using low-cost IoT technologies.