Robotic Solutions for Geothermal and Energy Sector Monitoring with IoT Random Forest Algorithm

Geothermal and energy industries are vital to global energy, delivering renewable and sustainable electricity. Continuous monitoring and data analysis are necessary for geothermal and energy facility efficiency. This research makes innovative techniques for monitoring these areas utilizing robots, the Internet of Things (IoT), and the Random Forest algorithm. Robotic technologies provide real-time data collecting in dangerous geothermal plants and energy infrastructure. These robots have sensors and cameras that measure temperature, pressure, vibration, and other vital characteristics. IoT allows these robots to wirelessly communicate data to a central monitoring system. Machine learning’s Random Forest algorithm analyzes incoming data streams. Complex and multidimensional datasets are its specialty, making it ideal for anomaly detection, equipment failure prediction, and operations optimization. Real-time insights from the algorithm improve geothermal and energy facility efficiency and safety. It describes the entire system architecture, including robotic unit design and deployment, IoT infrastructure, and Random Forest. It describes how this strategy reduces downtime, improves energy production, and improves operator and environmental safety.