Renewable energy data acquisition and analysis sofware tool based on IoT sensors network for solar irradiance or wind power density forecasting

被引：0

作者：

Kam, Olle Michel ^{[1
]}

Tanougast, Camel ^{[2
]}

Ramenah, Harry ^{[2
]}

机构：

[1] Univ Toulouse 2, Lab Rech Specialise Anal & Architecture Syst LAAS, Toulouse, France

[2] Univ Lorraine, Lab Genie Informat Prod & Maintenance LGIPM, Equipe SyLEE, Metz, France

来源：

2024 1ST INTERNATIONAL CONFERENCE ON SMART ENERGY SYSTEMS AND ARTIFICIAL INTELLIGENCE, SESAI 2024 | 2024年

关键词：

IoT; LoRaWAN; solar energy; wind energy; solar irradiance; wind speed; photovoltaic energy assessment; wind turbine energy assessment; Weibull function model; statistic model; Meteo-France data; VALIDATION; PARAMETERS; SYSTEM;

D O I：

10.1109/SESAI61023.2024.10599450

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper presents a renewable data acquisition and analysis software tool based on Internet of Things (IoT) sensors network for global solar irradiance and wind power density forecasting. This developed tool uses an adapted Weibull method to characterize global solar irradiation or wind speed variabilities to forecast of harvesting photovoltaic (PV) or wind turbine energies. The software implementation of the proposed forecasting model interfaced to weather sensors allows a realtime and intelligent management of deployed renewable energy systems in the France mainland territory.

引用

页码：154 / 158

页数：5

共 17 条

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