A Climate-Scenario-Aware Artificial Intelligence Framework for Predicting Future Building Energy Consumption Under Climate Change

Accurate building energy prediction is essential for climate-resilient design, retrofit planning, and long-term energy management. However, most machine-learning models are developed using historical weather data, implicitly assuming that future climatic conditions will remain similar to the past. This assumption is increasingly challenged by climate change, which is altering temperature patterns, solar exposure, humidity levels, and the frequency of extreme weather events. This study presents a climate-scenario-aware artificial intelligence framework that integrates future climate conditions into simulation-driven machine-learning development and validation. Using a UK hotel case study based on the Hilton Watford context, future weather scenarios were derived from CIBSE datasets informed by UKCP18 and CMIP6 climate projections. EnergyPlus version 23.2.0 simulations were performed under baseline, moderate-warming, high-warming, and heatwave stress-test scenarios to generate hourly building energy data. Random Forest, XGBoost 2.1.1, Multiple Linear Regression, and Multi-Layer Perceptron models were trained and evaluated using both Historical-Only and Climate-Scenario-Aware training approaches. Results show that models trained exclusively on historical conditions maintain high present-day accuracy but experience notable performance degradation under future climate scenarios, particularly for cooling demand and peak-load prediction. In contrast, Climate-Scenario-Aware models demonstrated improved robustness, reduced prediction errors, and greater physical consistency during extreme heatwave conditions while maintaining comparable performance under current climatic conditions. The proposed framework provides a reproducible methodology for developing climate-resilient AI models for building energy prediction and highlights the importance of incorporating future climate scenarios into model training and validation. The findings suggest that climate stress-testing should become a standard component of AI-based building energy analytics, digital twins, and long-term energy planning tools.