Data-Driven Load Prediction for Hospitals: Evaluating ML Models for Energy Management

This work aims to evaluate the crucial need for reliable, long-term electrical-load forecasting in hospitals to prevent power outages, which can cause the patient-fatalities, with reference to real-life case studies in India and other locations. Conventional approaches, such as ARIMA, are designed for short-term forecasting and do not deal with the nonlinear and continuous-dynamics of a hospital caused by the HVAC system, medical equipment, and emergencies. Experimental load data from one-year of hospital was used to evaluate seven ML/DL models: MLP, XGBoost, RF, SVR, LSTM, CNN-LSTM, and Transformer. The evaluation-criteria were RMSE, MAE, MAPE, and R². RF, XG Boost, CNN-LSTM, and SVR beat Transformer with RMSE=0.0434 ± 0.0003, MAE=0.0349 ± 0.0002, MAPE=0.0983 ± 0.0009, R²=0.8799 ± 0.0004. Both LSTM and MLP models showed similar results, with an RMSE of 0.0436 and an R² value of 0.8792. The novelty and superiority of this proposed work lie in the Transformer-based energy forecasting model, which uses self-attention to capture long-range dependencies, making it pioneering. Since convergence analysis shows steady loss curves, this variation is supported. The authentication loss plateaus between 25 and 35 epochs, indicating no overfitting. Over 20 separate trials, convergence stability is consistent with low variance. A Friedman test (χ² > 45, p0.05). It reduces energy consumption by 10–15%, improves energy management in healthcare, and reduces patient mortality worldwide.