AI can optimize hospital energy use without new infrastructure

Energy use in healthcare is a growing policy concern. Hospitals account for a significant share of public sector emissions, and many systems are under pressure to meet sustainability targets without compromising patient care. The study’s findings suggest that meaningful gains are possible using existing infrastructure, provided energy management becomes more intelligent.

Rising energy costs, growing pressure to decarbonize healthcare, and the increasing complexity of hospital operations have exposed the limits of traditional energy management systems. A new study suggests artificial intelligence may offer a viable path forward, not through futuristic infrastructure upgrades, but by making existing systems smarter, more predictive, and more efficient.

The study titled AI-Based Demand Forecasting and Load Balancing for Optimising Energy Use in Healthcare Systems: A Real Case Study, published in the journal Systems, evaluates how advanced machine learning models can forecast hospital energy demand with high accuracy and translate those forecasts into operational energy savings without disrupting clinical care.

Why traditional energy management fails in hospitals

Healthcare facilities present a uniquely difficult environment for energy management. Unlike offices or residential buildings, hospitals experience constant fluctuations driven by patient admissions, emergency activity, medical equipment usage, infection control requirements, and strict indoor climate standards. Heating, ventilation, air conditioning, lighting, and specialized equipment must operate reliably regardless of time of day or season.

The study highlights that traditional energy forecasting tools such as rule-based systems and linear statistical models struggle in this context. These methods assume relatively stable or predictable patterns, making them ill-suited to environments where demand can spike suddenly or follow complex non-linear trends. As a result, hospitals often over-provision energy to ensure reliability, leading to waste, higher costs, and unnecessary emissions.

To assess whether artificial intelligence can overcome these limitations, the researchers tested three forecasting approaches. The first was ARIMA, a widely used statistical model that performs well for linear time-series data. The second was Prophet, a forecasting tool designed to handle seasonality and missing data. The third was a Long Short-Term Memory neural network, a form of deep learning specifically designed to capture long-term dependencies and non-linear relationships in sequential data.

Using hourly energy consumption data covering electricity and gas use, the study found that ARIMA produced the largest forecasting errors, failing to adapt to the dynamic nature of hospital energy demand. Prophet improved accuracy by modeling seasonal trends but still struggled with short-term variability. LSTM, by contrast, delivered a step change in performance, achieving far lower error rates and explaining nearly all observed variability in demand.

This difference matters operationally. Inaccurate forecasts force energy managers to rely on conservative assumptions, increasing costs and reducing efficiency. More accurate predictions allow hospitals to anticipate demand, adjust schedules, and allocate resources with greater confidence.

How AI forecasting and optimization work together

Forecasting alone does not reduce energy consumption. The study connects accurate predictions to actionable energy management decisions. To do this, the researchers integrated a genetic algorithm, an optimization technique inspired by natural selection, into the forecasting framework.

The genetic algorithm uses predicted demand as an input and iteratively searches for the most efficient way to distribute energy loads across time and operational units. Rather than simply matching supply to demand, it minimizes imbalances, smooths load profiles, and identifies opportunities for load shifting without affecting essential services.

This combination proved especially effective when paired with the LSTM model. The deep learning forecasts provided a reliable picture of future demand, while the genetic algorithm translated that insight into optimized energy allocation strategies. The result was a system capable of adapting to fluctuating conditions while maintaining operational stability.

The study also addressed a growing concern around artificial intelligence in critical infrastructure: transparency. Complex models are often criticized as black boxes, making it difficult for facility managers to trust their outputs. To counter this, the researchers incorporated explainability techniques that identify which historical demand patterns most influence predictions.

The analysis showed that traditional models relied heavily on the most recent data points, limiting their ability to understand broader trends. The LSTM model, in contrast, drew on a wider range of historical information, including mid-range and long-term patterns. This broader contextual awareness explains its superior performance and provides operators with greater confidence in its recommendations.

By combining forecasting, optimization, and interpretability, the framework moves beyond experimental modeling into something closer to an operational decision-support system.

What the findings mean for healthcare sustainability

Energy use in healthcare is a growing policy concern. Hospitals account for a significant share of public sector emissions, and many systems are under pressure to meet sustainability targets without compromising patient care. The study’s findings suggest that meaningful gains are possible using existing infrastructure, provided energy management becomes more intelligent.

The Perth hospital case study revealed that heating was the dominant driver of gas consumption, while lighting and equipment loads were the main contributors to electricity demand. By aligning heating schedules with forecasted low-demand periods and adjusting lighting and equipment usage during off-peak hours, the AI-driven framework identified realistic opportunities for energy reduction.

Notably, these interventions operate at the operational level. They do not require new equipment, major retrofits, or changes to clinical workflows. Instead, they rely on better timing, smarter scheduling, and more informed decision-making. This lowers the barrier to adoption, particularly for older hospitals or facilities with limited capital budgets.

The framework is well suited for integration into existing building management systems. Forecasts can be generated automatically, optimization rules can guide load shifting, and continuous monitoring can refine predictions over time. This incremental approach allows healthcare facilities to adopt AI-driven energy management without large upfront disruption.

The implications also extend to resilience. More accurate demand forecasting reduces the risk of overloads and improves preparedness for extreme weather events or sudden demand surges. As healthcare systems face increasing strain from climate change and population growth, these capabilities become strategically important.

The study also positions its findings within a broader shift toward data-driven healthcare operations. While artificial intelligence is often associated with diagnostics or patient monitoring, this research shows its potential in the less visible but equally critical domain of infrastructure management.

Looking ahead, the authors identify several directions for future work, including real-time deployment, hybrid forecasting models, and reinforcement learning for continuous optimization. They also note the potential to extend the framework across multiple hospitals to test scalability and robustness under diverse conditions.