LSTM-Based Electricity Demand Forecasting in Smart and Sustainable Hospitality Buildings

Abstract

Accurate short-term load forecasting (STLF) is essential for energy management in buildings, yet remains challenging due to the nonlinear interactions among weather, occupancy, and operational patterns. This study presents a reproducible forecasting pipeline applied as a case study to a single anonymized hotel in Greece, representing a highly variable building-scale load. Three heterogeneous data streams were programmatically ingested and aligned: distribution-operator smart meter telemetry (15 min intervals aggregated to daily active energy), enterprise guest-night counts as an occupancy proxy, and meteorological observations from the National Observatory of Athens (NOA). Following rigorous preprocessing, feature construction incorporated lagged demand, calendar encodings, and exogenous drivers. Forecasting was performed with a stacked LSTM architecture (BiLSTM → LSTM → LSTM with dropout and a compact dense head), trained and validated under a leakage-safe chronological split. A bounded random hyperparameter search of forty configurations was tracked in MLflow 3.5.0 to ensure full reproducibility. The best model achieved RMSE of 4.71 kWh, MAE of 3.48 kWh, and MAPE of 3.29% on the hold-out test set, with stable training and robust diagnostics. The findings confirm that compact recurrent networks can deliver accurate and transparent hotel-level forecasts, providing a practical template for operational energy planning and sustainability reporting. Future research should extend this case study to multi-building portfolios and hybrid deep learning architectures.