Virtual Sensor Based on Temporal Trend: Short-Term Monitoring and Forecasting of Meteorological Variables Using Linear Regression and the OpenWeatherMap API
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2026
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| _version_ | 1866901369062948864 |
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| author | Souza, Vitor Amadeu |
| author_facet | Souza, Vitor Amadeu |
| contents | <div class="markdown markdown-main-panel enable-updated-hr-color"> <p>This article presents the development of a virtual sensor for meteorological variables implemented in Python, with real-time data collection via the OpenWeatherMap API, sliding-window in-memory storage, and short-term forecasting of temperature and relative humidity through linear regression. The system architecture integrates a Flask web server, a machine learning module based on the Scikit-learn library, and an interactive graphical interface built with Chart.js, which displays the observed historical record alongside model-generated forecasts. The results demonstrate that the system is capable of continuously monitoring variables such as temperature, relative humidity, atmospheric pressure, and wind speed, producing short-term forecasts based on recent trends. The proposed approach is computationally lightweight, straightforward to deploy, and constitutes a viable alternative for real-time environmental monitoring applications operating under limited computational resources.</p> </div> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_19610313 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Virtual Sensor Based on Temporal Trend: Short-Term Monitoring and Forecasting of Meteorological Variables Using Linear Regression and the OpenWeatherMap API Souza, Vitor Amadeu Virtual sensor Linear regression OpenWeatherMap Flask meteorological monitoring Python <div class="markdown markdown-main-panel enable-updated-hr-color"> <p>This article presents the development of a virtual sensor for meteorological variables implemented in Python, with real-time data collection via the OpenWeatherMap API, sliding-window in-memory storage, and short-term forecasting of temperature and relative humidity through linear regression. The system architecture integrates a Flask web server, a machine learning module based on the Scikit-learn library, and an interactive graphical interface built with Chart.js, which displays the observed historical record alongside model-generated forecasts. The results demonstrate that the system is capable of continuously monitoring variables such as temperature, relative humidity, atmospheric pressure, and wind speed, producing short-term forecasts based on recent trends. The proposed approach is computationally lightweight, straightforward to deploy, and constitutes a viable alternative for real-time environmental monitoring applications operating under limited computational resources.</p> </div> |
| title | Virtual Sensor Based on Temporal Trend: Short-Term Monitoring and Forecasting of Meteorological Variables Using Linear Regression and the OpenWeatherMap API |
| topic | Virtual sensor Linear regression OpenWeatherMap Flask meteorological monitoring Python |
| url | https://doi.org/10.5281/zenodo.19610313 |