A comprehensive IoT cloud-based wind station ready for real-time measurements and artificial intelligence integration
Accurate and efficient wind measurement and monitoring are crucial for various applications, including renewable energy, meteorology, public safety, and environmental research. This paper presents an integrated approach for a cloud-computing Internet of Things-based Automated Weather Station, with e...
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| Language: | English |
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Elsevier
2024-12-01
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| Series: | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772671124004418 |
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| author | Décio Alves Fábio Mendonça Sheikh Shanawaz Mostafa Fernando Morgado-Dias |
| author_facet | Décio Alves Fábio Mendonça Sheikh Shanawaz Mostafa Fernando Morgado-Dias |
| author_sort | Décio Alves |
| collection | DOAJ |
| description | Accurate and efficient wind measurement and monitoring are crucial for various applications, including renewable energy, meteorology, public safety, and environmental research. This paper presents an integrated approach for a cloud-computing Internet of Things-based Automated Weather Station, with edge devices, designed to provide real-time wind measurements in diverse environments. The presented system comprises a dedicated station frame, a self-sufficient solar power setup, an ultrasonic wind sensor, a data transmission node, cloud computing capabilities, and an end-user visualization application. Calibration procedures reduced the maximum mean error of the ultrasonic wind sensors from 0.7 m/s to 0.2 m/s, achieving a 71% improvement in measurement accuracy. Using this method, all station data are processed every 3 seconds and stored ready for Artificial Intelligence usage with an approximate latency of 150–300 milliseconds, representing up to an 85% reduction in processing delay compared to similar solutions with over 1-second latencies. The lightweight and resistant frame design facilitates easy deployment, while the power setup with sub-watt efficiency ensures a reliable energy source. The ultrasonic wind sensor was subjected to calibration procedures and design improvements to increase its performance under specific rain conditions. Internet of Things communication via Hypertext Transfer Protocol enables efficient data transmission between the Automated Weather Station and the cloud server, which processes and stores the data for future analysis. The responsive and auto-adaptive web application allows user-friendly data visualization, making the wind data easily accessible and interpretable. The proposed cloud-computing Internet of Things-based Automated Weather Station framework demonstrates significant potential for accurate and efficient wind measurement and monitoring, paving the way for future advancements in high temporal resolution wind monitoring systems capable of producing big data prepared for subsequent machine learning model approaches. |
| format | Article |
| id | doaj-art-07cf5526fdb64d36bd5ebb6be65e69ea |
| institution | OA Journals |
| issn | 2772-6711 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | e-Prime: Advances in Electrical Engineering, Electronics and Energy |
| spelling | doaj-art-07cf5526fdb64d36bd5ebb6be65e69ea2025-08-20T02:35:41ZengElseviere-Prime: Advances in Electrical Engineering, Electronics and Energy2772-67112024-12-011010086210.1016/j.prime.2024.100862A comprehensive IoT cloud-based wind station ready for real-time measurements and artificial intelligence integrationDécio Alves0Fábio Mendonça1Sheikh Shanawaz Mostafa2Fernando Morgado-Dias3University of Madeira, 9000-082 Funchal, Portugal; Campus Universitário da Penteada 9020-105 Funchal, Portugal; Interactive Technologies Institute (ITI/LARSyS and ARDITI), Edif. Madeira Tecnopolo, Caminho da Penteada piso -2, 9020-105 Funchal, Portugal; Corresponding author.University of Madeira, 9000-082 Funchal, Portugal; Campus Universitário da Penteada 9020-105 Funchal, Portugal; Interactive Technologies Institute (ITI/LARSyS and ARDITI), Edif. Madeira Tecnopolo, Caminho da Penteada piso -2, 9020-105 Funchal, PortugalInteractive Technologies Institute (ITI/LARSyS and ARDITI), Edif. Madeira Tecnopolo, Caminho da Penteada piso -2, 9020-105 Funchal, PortugalUniversity of Madeira, 9000-082 Funchal, Portugal; Campus Universitário da Penteada 9020-105 Funchal, Portugal; Interactive Technologies Institute (ITI/LARSyS and ARDITI), Edif. Madeira Tecnopolo, Caminho da Penteada piso -2, 9020-105 Funchal, PortugalAccurate and efficient wind measurement and monitoring are crucial for various applications, including renewable energy, meteorology, public safety, and environmental research. This paper presents an integrated approach for a cloud-computing Internet of Things-based Automated Weather Station, with edge devices, designed to provide real-time wind measurements in diverse environments. The presented system comprises a dedicated station frame, a self-sufficient solar power setup, an ultrasonic wind sensor, a data transmission node, cloud computing capabilities, and an end-user visualization application. Calibration procedures reduced the maximum mean error of the ultrasonic wind sensors from 0.7 m/s to 0.2 m/s, achieving a 71% improvement in measurement accuracy. Using this method, all station data are processed every 3 seconds and stored ready for Artificial Intelligence usage with an approximate latency of 150–300 milliseconds, representing up to an 85% reduction in processing delay compared to similar solutions with over 1-second latencies. The lightweight and resistant frame design facilitates easy deployment, while the power setup with sub-watt efficiency ensures a reliable energy source. The ultrasonic wind sensor was subjected to calibration procedures and design improvements to increase its performance under specific rain conditions. Internet of Things communication via Hypertext Transfer Protocol enables efficient data transmission between the Automated Weather Station and the cloud server, which processes and stores the data for future analysis. The responsive and auto-adaptive web application allows user-friendly data visualization, making the wind data easily accessible and interpretable. The proposed cloud-computing Internet of Things-based Automated Weather Station framework demonstrates significant potential for accurate and efficient wind measurement and monitoring, paving the way for future advancements in high temporal resolution wind monitoring systems capable of producing big data prepared for subsequent machine learning model approaches.http://www.sciencedirect.com/science/article/pii/S2772671124004418Weather stationWind measurementsIoT wind stationCloud-base weather monitoring |
| spellingShingle | Décio Alves Fábio Mendonça Sheikh Shanawaz Mostafa Fernando Morgado-Dias A comprehensive IoT cloud-based wind station ready for real-time measurements and artificial intelligence integration e-Prime: Advances in Electrical Engineering, Electronics and Energy Weather station Wind measurements IoT wind station Cloud-base weather monitoring |
| title | A comprehensive IoT cloud-based wind station ready for real-time measurements and artificial intelligence integration |
| title_full | A comprehensive IoT cloud-based wind station ready for real-time measurements and artificial intelligence integration |
| title_fullStr | A comprehensive IoT cloud-based wind station ready for real-time measurements and artificial intelligence integration |
| title_full_unstemmed | A comprehensive IoT cloud-based wind station ready for real-time measurements and artificial intelligence integration |
| title_short | A comprehensive IoT cloud-based wind station ready for real-time measurements and artificial intelligence integration |
| title_sort | comprehensive iot cloud based wind station ready for real time measurements and artificial intelligence integration |
| topic | Weather station Wind measurements IoT wind station Cloud-base weather monitoring |
| url | http://www.sciencedirect.com/science/article/pii/S2772671124004418 |
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