Abstract : Due to different increasing risk factors like Global Warming, Climate change, Heavy metal, Plastic, Toxic waste etc. in recent years, the increasing frequency and intensity of natural disasters have posed a significant challenge to disaster prediction, preparedness and response. Early prediction and detection of potential disaster events are crucial for minimizing the impact on communities and infrastructure. This abstract presents a novel approach that combines simulation techniques and automatic division strategies to enhance disaster prediction and minimization by diverting and optimizing use and distribution of energy resources by leveraging technologies like IOT or Interconnected networking devices. The proposed methodology utilizes advanced simulation models to analyze historical data, weather patterns, geological information, information and other relevant factors to forecast the occurrence of potential disasters such as floods, earthquakes, hurricanes or wildfires. By simulating various different scenarios, the system can predict the likelihood of disasters with increased accuracy which aids in proactive disaster management. In recent years, interconnected network systems like IOT, Wi-Fi, Zigbee, Z-Wave, BLE, Matter etc. has increased in popularity. They can be either hard wired or wirelessly connected and can be controlled automatic protocols. These systems are present in different smart homes in comparatively moderate to large quantities and consume a moderate amount of electricity to ensure convenience. In case of large scale to medium scale disaster the main resource is food, shelter and Electricity to run lifesaving equipment. This electricity or energy resources can be can be managed optimally in panic situations by cutting off or reduce extravagant luxury-oriented devices. So, in these kind of situations the automatic energy diversion streams may be very crucial to save up important resource like electricity. The automatic diversion systems are integrated into critical infrastructure such as transportation networks, water management systems and power grids. These systems are programmed to dynamically redirect resources and divert critical services in response to the predicted disaster events. By implementing autonomous or semi-autonomous decision-making algorithms, these systems can rapidly adapt and optimize the allocation of resources to safeguard communities and essential infrastructure. The pre-determination and automatic diversion process rely on real-time data collection, fetching and analysis from a network of sensor, satellite, weather stations and other monitoring devices. Artificial Intelligence and Machine Learning algorithms play a pivotal role in continuously updating and refining the models based on new data inputs. Further, the IOT infrastructure facilitates seamless communication and co-ordination among energy harvesting devices, optimizing their performance and ensuring maximum energy extraction. Remote monitoring and control capabilities offered by IOT platforms enable operators to fine-tune system parameters for optimal output. This also emphasizes the significance of energy storage solutions, as renewable energy sources often exhibit intermittent generation patterns. IOT based energy harvesting systems can be integrated with advanced storage technologies like batteries, supercapacitors or energy management systems, ensuring a consistent and reliable energy supply. There is a potential for energy harvesting from unconventional sources, such as ambient vibrations and waste heat etc. IOT enabled smart buildings, bridges and industrial equipment can capture and convert otherwise wasted energy into usable electrical power. The successful implementation of IOT-enabled energy harvesting systems offers numerous advantages: 1. including reduced reliance on conventional energy sources 2. minimized environmental impact 3. increased energy efficiency. The scalability and flexibility of IOT technologies allow for the seamless integration of these systems into existing infrastructure. However, challenges such as data security, device interoperability and the optimization of energy conversion efficiencies must be addressed to ensure the widespread adoption of IoT-based energy harvesting solutions properly.

Keyword :IOT, Renewable Energy, Energy Harvesting, Energy Diversion, Critical Energy Management, Artificial Intelligence, Disaster Prediction.