Short-Term Load Forecasting for Hospitals Using Transformer-Based Deep Learning with Temporal Memory and Statistics Features
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Abstract
Reliable electricity is vital for hospitals, where even short disruptions can affect patient safety and daily operations. This paper presents a single-hospital case study on short-term load forecasting using real half-hourly data from Mae Moh Hospital in Thailand, covering October 2023 to February 2025. To avoid future data leakage and improve model robustness, we design a leakage-aware feature-engineering pipeline that integrates time-of-day cycles, operational context (weekend, holiday, working hour), and temporal memory with statistical features. A 48-step input window (24 hours) is used to predict the next two steps (+30, +60 minutes). We evaluate several compact Transformer-based hybrids (Transformer+LSTM, Transformer+GRU, Transformer+CNN) against baseline methods. Results show that the Transformer+LSTM achieves the best performance (RMSE = 0.45426, MAE = 0.26388, R2 = 0.92571), capturing both daily cycles and fine-grained transitions in hospital demand. The proposed work flow is reproducible and practical for real-world use, enabling hospitals to improve energy planning and prepare for integration into future Virtual Power Plant (VPP) programs.
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