IOT-DRIVEN OPTIMIZATION OF SELF-SUFFICIENT SOLAR ENERGY SYSTEMS THROUGH METEOROLOGICAL DATA INTEGRATION
DOI:
https://doi.org/10.61164/rmnm.v10i1.4031Keywords:
Solar Energy, Off-Grid Model, MicroPython, Python, Embedded Systems, IoT, Solar RadiationAbstract
Around 900,000 people do not have access to electricity in Brazil, according to the IBGE, meanwhile, a significant portion of the population reliant on hydroelectric power faces recurring energy price hikes driven by water scarcity and systemic mismanagement. Decentralized solar energy systems offer a promising alternative; however, their intermittent nature due to seasonal variability can compromise reliability for off-grid users. To address this challenge, this study proposes an IoT-enabled embedded system that integrates meteorological data with a solar radiation prediction algorithm. The system dynamically correlates energy generation forecasts with consumption patterns to provide real-time recommendations for optimized usage, empowering users to adjust consumption proactively. During testing, the algorithm demonstrated strong alignment between predicted and actual solar energy generation, highlighting its potential to enhance energy resilience in off-grid scenarios.
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