Advanced Machine Learning Techniques for Optimizing Renewable Energy Systems in Healthcare Facilities

Optimizing renewable energy systems in healthcare facilities via use of modern ML algorithms to improve energy efficiency along with sustainability is goal of this research. Integrating machine learning provides a potential solution in case of optimizing energy consumption along with management, which is particularly relevant for healthcare institutions that are looking to decrease their operating expenses and carbon footprint by using renewable energy sources. Optimal deployment of renewable ES like solar along with wind may be achieved by the analysis of consumption patterns, prediction of future energy demands, and the investigation of different ML methods, such as neural networks, reinforcement learning, and predictive analytics. Research in this area aims to increase hospital renewable energy systems' efficiency and dependability by creating models that take use of environmental factors, real-time energy consumption indicators, and historical data. The results imply that healthcare facilities may save money, have less of an effect on the environment, and be more resilient if they use sophisticated machine learning methods to perk upcompetence of their renewable energy systems. The study emphasizes the importance of hyperparameter configuration in improving machine learning model accuracy, with a proposed approach achieving 99 % training and 96 % testing accuracy, outperforming conventional methods and outperforming other techniques.