High-Accuracy Energy Forecasting for Sustainable Hospitality: A Hybrid Ensemble Machine Learning Approach to 50-Year Retrofit Analysis in Sub-Tropical Hotels

Accurate energy forecasting is critical for the financial and environmental sustainability of the hospitality sector, particularly in energy-intensive subtropical climates. This research addresses a significant gap by conducting a comprehensive, comparative analysis of six machine learning algorithms—Artificial Neural Networks (ANN), Random Forest (RF), XGBoost, Radial Basis Function (RBF), Autoencoder, and Decision Trees—to predict the hourly energy consumption of a hotel in Cuba. We significantly enhance predictive performance through a novel hybrid ensemble scheme, integrating voting, stacking, and blending techniques. Furthermore, this study pioneers a long-term forecasting methodology by utilizing a Long Short-Term Memory (LSTM) model to project the hotel’s energy demand over a 50-year horizon, providing the strategic insight necessary for evaluating major retrofits. Our results demonstrate that ensemble methods, particularly blending, achieve superior accuracy and stability, with correlation coefficients up to 0.975 and the lowest error metrics. The subsequent high-fidelity predictions, including an analysis revealing a minimal specific CO2 emission of 0.025 kg from natural gas use, provide a quantitative foundation for formulating sustainable energy policies, incentivizing investment in efficient technologies, and ensuring that long-term emission reduction targets are both financially viable and technically robust.