primary topic: Wind speed monitoring system for GFS and ECMWF data using AWS Grafana
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HTML Summary of #20678 Wind speed monitoring system for GFS and ECMWF data using AWS Grafana
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primary topic: Wind speed monitoring system for GFS and ECMWF data using AWS Grafana

Wind speed monitoring system for GFS and ECMWF data using AWS Grafana
abstract: Effective wind speed monitoring is critical for industries such as renewable energy and weather forecasting. This paper introduces an advanced Wind Speed Monitoring System that combines AWS Grafana with data from the Global Forecast System (GFS) and the European Centre for Medium-Range Weather Forecasts (ECMWF), integrated with a Long Short-Term Memory (LSTM) prediction model for enhanced forecasting accuracy. The system leverages high-resolution wind speed data from GFS and ECMWF, renowned for their reliability and extensive global coverage. This data is ingested into Amazon Web Services (AWS) for processing and storage using Amazon Timestream, a time-series database optimized for handling large volumes of data efficiently. To improve the predictive capabilities of the system, an LSTM neural network model is employed. LSTM, a type of recurrent neural network (RNN), excels at capturing temporal dependencies and patterns in time-series data, making it ideal for predicting wind speed. The LSTM model is trained using historical wind speed data from GFS and ECMWF, allowing it to generate accurate short-term and long-term wind speed forecasts. A Visualization Dashboard (VD) using AWS Grafana was developed with dynamic and interactive real live and LSTM prediction of wind speed data. The main shows a wind speed monitoring system home page. Other pages are the tropical depression of wind speed, parameters data of wind speed, the model performance comparison page of the monitoring system and model comparison page. The Grafana VD shows robust querying and visualization of trends analysis, compare forecasts from GFS, ECMWF, and the LSTM model, and set up alerts for significant changes in wind speed. This Wind Speed Monitoring System is significant in providing a comprehensive of accurate wind speed forecasting and monitoring. The system's architecture, implementation, and benefits are discussed, highlighting its potential to enhance decision-making processes across various in…
author list: authors
issue: 1
status: peerReviewed
status: published
volume: 3322
date: 2025
is part of: repository
is part of: AIP Conference Proceedings
type: AcademicArticle
type: Article
type: ConferenceItemEPrint
type: EPrint

American Institute of Physics
type: Organization

Nooritawati Md Tahir
family name: Tahir
given name: Nooritawati Md
type: Person

Murizah Kassim
family name: Kassim
given name: Murizah
type: Person

Nor Farisha Muhamad Krishnan
family name: Krishnan
given name: Nor Farisha Muhamad
type: Person

Tengku Nazmi Tengku Asmawi
family name: Asmawi
given name: Tengku Nazmi Tengku
type: Person

Marina Yusoff
family name: Yusoff
given name: Marina
type: Person

Farahida Hanim Mausor
family name: Mausor
given name: Farahida Hanim
type: Person

Jasni Mohamad Zain
family name: Zain
given name: Jasni Mohamad
type: Person

Abdul Kadir Jumaat
family name: Jumaat
given name: Abdul Kadir
type: Person

Nazleeni Samiha Haron
family name: Haron
given name: Nazleeni Samiha
type: Person

Jafreezal Jaafar
family name: Jaafar
given name: Jafreezal
type: Person

Siti Sara Ibrahim
family name: Ibrahim
given name: Siti Sara
type: Person

AIP Conference Proceedings
issn: 0094243X
type: Collection