IoT-Based Predictive Maintenance for Solar and Wind Energy Systems Using Machine Learning Models
DOI:
https://doi.org/10.54228/mjaret0624034Keywords:
Predictive Maintenance; Internet of Things (IoT); Machine Learning, Solar Energy; Wind Energy; Renewable Energy Systems; Failure Prediction; Smart Monitoring.Abstract
When the IoT technology is connected with the machine learning models, renewable energy systems, such as solar and wind energy, can benefit from a reliable predictive maintenance. This paper describes an IoT based predictive maintenance system for the smart collection of the real time operation data of solar panels and wind turbines. Raw data contained in the system goes through data cleansing and is processed with the help of sophisticated ML algorithms such as LSTM and Random Forest for failure forecast and proper scheduling of maintenance. It is stated that the proposed system would cut down possible on time by 30%, increase energy effectiveness by 25% and also decrease maintenance consumption by 20%. The general experiment outcomes prove that this technique reaches the desired levels and yields an almost 92% of accuracy towards the expectation of enhancing the dependability and capability of renewable energy structures.
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