Journal Article

AI-Based Demand Forecasting and Load Balancing for Optimising Energy Use in Healthcare Systems: A Real Case Study

Patel, Isha, Rahimi, Iman

2026

Overview

This paper addresses the critical need for efficient energy management in healthcare facilities, where fluctuating energy demands pose challenges to both operational reliability and sustainability objectives. Traditional energy management approaches often fall short in healthcare settings, resulting in inefficiencies and increased operational costs. To address this gap, the paper explores AI-driven methods for demand forecasting and load balancing and proposes an integrated framework combining Long Short-Term Memory (LSTM) networks, a genetic algorithm (GA), and SHAP (Shapley Additive Explanations), specifically tailored for healthcare energy management. While LSTM has been widely applied in time-series forecasting, its use for healthcare energy demand prediction remains relatively underexplored. In this study, LSTM is shown to significantly outperform conventional forecasting models, including ARIMA and Prophet, in capturing complex and non-linear demand patterns. Experimental results demonstrate that the LSTM model achieved a Mean Absolute Error (MAE) of 21.69, a Root Mean Square Error (RMSE) of 29.96, and an R2 of approximately 0.98, compared to Prophet (MAE: 59.78, RMSE: 81.22, R2 ≈ 0.86) and ARIMA (MAE: 87.73, RMSE: 125.22, R2 ≈ 0.66), confirming its superior predictive performance. The genetic algorithm is employed both to support forecasting optimisation and to enhance load balancing strategies, enabling adaptive energy allocation under dynamic operating conditions. Furthermore, SHAP analysis is used to provide interpretable, within-model insights into feature contributions, improving transparency and trust in AI-driven energy decision-making. Overall, the proposed LSTM–GA–SHAP framework improves forecasting accuracy, supports efficient energy utilisation, and contributes to sustainability in healthcare environments. Future work will explore real-time deployment and further integration with reinforcement learning to enable continuous optimisation.

• Publisher: MDPI AG
• Subject: Accuracy / Alternative energy sources / Analysis / Artificial intelligence / Autoregressive moving-average models / Case studies / Cost control / Cost reduction / Demand / demand forecasting / Distributed processing / Efficiency / Energy consumption / Energy demand / Energy management / Energy use / Energy utilization / Environmental conditions / Forecasting / Forecasting techniques / genetic algorithm / Genetic algorithms / Health facilities / Health systems agencies / healthcare energy optimisation / Literature reviews / Load balancing / Load balancing (Computers) / Machine learning / Mathematical optimization / Medical equipment / Neural networks / Optimization / Real time / renewable energy integration / Renewable resources / Root-mean-square errors / Software / Sustainability / Technology application / Time series