Integrating Random Forest Prediction for Energy Optimization in Solar-Powered Environmental Monitoring

In light of the essential need for persistent, real-time monitoring of natural environments, our research introduces an innovative approach that integrates a predictive solar energy model in optimizing solar-powered environmental monitoring efficiency with energy constraints. We address the challenge of solar energy variability by forecasting solar energy availability based on the Random Forest model. Then, we integrate the prediction ability into the Integer Linear Program optimization framework of the monitoring efficiency subjected to energy constraints. Experimental results demonstrate our approach's effectiveness compared to traditional methods, underscoring its potential for enhancing sensor networks' sustainability and operational efficiency in natural environments.