Podrobná bibliografie
| Název: |
Design of an ESP32-Based Smart Meteorological Data Collection Station for Renewable Energy Applications. |
| Autoři: |
Karapapak, Bedir Kaan, Küçüker, Mevlüt Eren, Topal, Mert Ali, Emek, Eren, Soukar, Mohammad |
| Zdroj: |
ADBA Computer Science; Jan2026, Vol. 3 Issue 1, p51-56, 6p |
| Témata: |
AUTOMATIC meteorological stations, RENEWABLE energy sources, ENVIRONMENTAL monitoring, SOLAR energy, METEOROLOGICAL databases, BLUETOOTH technology, WIND speed measurement, MICROCONTROLLERS |
| Abstrakt: |
This study presents the design and implementation of an ESP32 microcontroller based, modular smart meteorological data collection station developed to increase the efficiency of renewable energy systems and monitor environmental data at a low cost. The system is designed as an alternative to high-cost industrial stations. Core hardware components include BMP280 (temperature, pressure), wind speed (Hall Effect anemometer), and wind direction (Hall Effect wind vane) sensors. ESP32 is used as the central control unit with its built-in Wi-Fi/Bluetooth features; it processes data in string format and transmits it to a Python-based desktop interface via Bluetooth serial communication protocol (at a frequency of 1 Hz). Additionally, a one-week circular storage logic (Circular Buffer) was created using the LittleFS file system on the ESP32 for uninterrupted data storage. A solar panel and battery management system were designed for off-grid operation capacity. As a result of validation tests, it was proven that the system provides high stability in data transmission and that linear regression-based calibration (over 50% error improvement) is mandatory to reach professional standards, especially in atmospheric pressure measurements. This low-cost and energy-efficient platform aims to provide a scalable, domestic data collection infrastructure for renewable energy sites and smart agriculture projects. [ABSTRACT FROM AUTHOR] |
|
Copyright of ADBA Computer Science is the property of ADBA Information Technology & Publishing Limited Company and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Databáze: |
Complementary Index |