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Related Experiment Video

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In Situ Soil Moisture Sensors in Undisturbed Soils

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Published on: November 18, 2022

Scalable Lightweight IoT-Based Smart Weather Measurement System.

Abdullah Albuali¹, Ramasamy Srinivasagan², Ahmed Aljughaiman¹

¹Department of Computer Networks and Communications, College of Computer Sciences and Information Technology, King Faisal University, Al-Ahsa 31982, Saudi Arabia.

Sensors (Basel, Switzerland)

|July 8, 2023

Summary

This study introduces a low-cost, AI-powered weather station for wind turbine energy generators. It provides accurate, localized weather data and forecasts, improving clean energy production.

Keywords:

conventional neural network decision tree low-cost weather station meteorology tiny machine learning (TinyML)wind speed wind velocity

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Published on: March 25, 2019

Area of Science:

Engineering
Computer Science
Environmental Science

Background:

Internet of Things (IoT) enables remote monitoring across sectors like energy.
Existing weather stations are costly and lack customization for specific applications like wind turbine energy generation (WTEG).
Localized weather data is crucial for optimizing clean energy production, but current forecasting methods are often inefficient.

Purpose of the Study:

To present a cost-effective, AI-driven weather station for WTEG applications.
To enable distributed, localized weather monitoring and forecasting.
To improve the efficiency and accuracy of clean energy production through precise weather data.

Main Methods:

Developed a low-cost weather station with heterogeneous nodes and controllers for WTEG areas.
Integrated an artificial intelligence (AI) algorithm for data analysis and forecasting.
Utilized Bluetooth low energy (BLE) for efficient data transmission.

Main Results:

The proposed system accurately measures wind direction (WD), wind velocity (WV), temperature, pressure, mean sea level, and relative humidity.
Nowcast measurements achieved 95% accuracy for WV and 92% for WD.
The system's performance aligns with the standards of the National Meteorological Center (NMC).

Conclusions:

The AI-powered, low-cost weather station effectively enhances clean energy production for WTEGs.
Distributed, localized weather monitoring provides superior accuracy compared to traditional methods.
The developed system offers a practical and affordable solution for optimizing renewable energy resources.