Machine Learning for Improving Renewable Energy: Integrating Solar Power for Continuous Operation of Smart Sensors During Natural Calamities

Abstract

This research tackles the critical issue of ensuring reliable data collection during natural disasters, such as floods, by calibrating and integrating smart sensors with renewable energy systems. Natural disasters often disrupt power supplies, posing significant challenges to the continuous operation and data availability of these sensors. To address this challenge, the study proposes leveraging renewable solar energy as a sustainable and reliable power source for smart sensors. By incorporating solar energy systems, the framework guarantees that sensors remain operational even in extreme conditions, ensuring that crucial environmental data is consistently accessible for rescue and recovery efforts. The research introduces an innovative approach that employs renewable energy to sustain the functionality of smart sensors during disasters. The framework includes models designed to manage uncertainties in disaster scenarios and optimize energy use for sensor operation. Additionally, the machine learning forecasting tool Prophet is utilized to improve the accuracy of predictions related to energy consumption and sensor performance. Prophet’s strength in handling time series data and generating reliable forecasts is vital for this application.