Harnessing Hybrid Energy with a Floating Waterwheel System: "A Synergistic Model for Riverine and Coastal Applications"

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Bol The Hydro and Wind Energy Monitoring System is an IoT-integrated solution using Arduino to track and analyze renewable energy generation from hydro and wind sources. The system monitors the voltage generated by wind turbines and hydroelectric generators, storing the harvested energy in a battery for later use. Real-time data, including generated voltage and water flow rate, is uploaded to the Thing Speak cloud for remote access and monitoring. A flow sensor is used to measure the rate of water flow, which directly impacts hydro energy generation. Wind energy is monitored by tracking the turbine's rotational speed and output voltage. The system leverages machine learning to predict future energy generation based on historical data stored in Thing Speak, enabling proactive energy management and optimization. Using random forest algorithm, predictive analytics of system can estimate power output under different environmental conditions, improving efficiency and reliability. The IoT integration ensures continuous data collection and remote visualization, making it an effective renewable energy management system.

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The Hydro and Wind Energy Monitoring System is an IoT-integrated solution using Arduino to track and analyze renewable energy generation from hydro and wind sources. The system monitors the voltage generated by wind turbines and hydroelectric generators, storing the harvested energy in a battery for later use. Real-time data, including generated voltage and water flow rate, is uploaded to the Thing Speak cloud for remote access and monitoring. A flow sensor is used to measure the rate of water flow, which directly impacts hydro energy generation. Wind energy is monitored by tracking the turbine's rotational speed and output voltage. The system leverages machine learning to predict future energy generation based on historical data stored in Thing Speak, enabling proactive energy management and optimization. Using random forest algorithm, predictive analytics of system can estimate power output under different environmental conditions, improving efficiency and reliability. The IoT integration ensures continuous data collection and remote visualization, making it an effective renewable energy management system.


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  • 9786208448974
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