AI-Based Demand Forecasting and Load Balancing for Optimising Energy use in Healthcare Systems: A real case study
2025-07-09
Summary
The article explores AI-driven techniques for optimizing energy management in healthcare facilities, focusing on demand forecasting and load balancing. It introduces a framework combining Long Short-Term Memory (LSTM) neural networks, genetic algorithms, and SHAP (Shapley Additive Explanations) for improved energy efficiency. The study found that LSTM outperforms traditional models like ARIMA and Prophet in predicting complex energy demand patterns.
Why This Matters
Efficient energy management is crucial in healthcare due to fluctuating demands and high costs. This study demonstrates the potential of AI to enhance operational efficiency and sustainability by accurately forecasting energy needs and optimizing distribution. As healthcare systems strive for cost reduction and environmental sustainability, such AI-driven solutions offer scalable and adaptable approaches.
How You Can Use This Info
Professionals in healthcare management can leverage AI-based solutions to better predict and manage energy usage, reducing costs and improving sustainability. Implementing AI-driven forecasting and load balancing can lead to more efficient resource allocation and operational stability. Additionally, understanding the role of advanced models like LSTM can aid in future-proofing energy strategies in dynamic environments.