Real-Time Energy Consumption Insights for Hospitals with IoT and Machine Learning

Reducing operating expenses and minimizing environmental effects requires efficient hospital energy management. This research provides a comprehensive approach for tracking and analyzing energy consumption in real time. With the Internet of Things (IoT) and Machine Learning, especially Support Vector Regression (SVR). Energy consumption, environmental factors, and operating parameters are continually monitored by the proposed system's network of IoT sensors. A centralized cloud platform receives data streams from these sensors in real-time, with high resolution. A powerful machine learning method, SVR, processes and analyzes data. It is well known for its accuracy in predicting continuous variables. SVR models energy consumption trends, spot outliers, and predict future energy use. The predictive features of the system allow hospital managers to spot inefficiencies, forecast peak energy needs, and take proactive actions to optimize energy consumption. Energy data can be easily interpreted, and decisions can be made with the help of the system's visualization and analytics features. This system's combination of IoT and SVR offers a scalable and inexpensive method to improve healthcare facilities' energy efficiency. According to experimental data from pilot installations, there was a notable improvement in overall energy management techniques and a considerable decrease in energy waste. According to this research, modern IoT and machine learning technology may drive sustainable hospital operations and environmental stewardship.