Osei-Owusu, Justine and Bahadori-Jahromi, Ali ORCID: https://orcid.org/0000-0003-0405-7146 (2026) A Climate-Scenario-Aware Artificial Intelligence framework for predicting future building energy consumption under climate change. Sustainability, 18 (13).

PDF A Climate-Scenario-Aware Artificial Intelligence Framework for Predicting Future Building Energy Consumption Under Climate Change- July 2026.pdf - Published Version Restricted to Repository staff only Download (2MB)

Abstract

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.

Item Type:	Article
Identifier:	10.3390/su18136893
Additional Information:	This peer-reviewed article presents a climate-scenario-aware AI framework combining future climate projections, EnergyPlus simulations and machine learning to support climate-resilient building design, retrofit planning and urban energy management.
Keywords:	building energy simulation; building energy prediction; climate change; machine learning; weather morphing; EnergyPlus 23.2.0; XGBoost 2.1.1; climate stress-testing; non-stationarity; hotel energy performance; urban sustainability
Date Deposited:	09 Sep 2026
Dates:	Date Publication status 7 July 2026 Published 16 June 2026 Accepted
School, department or research centre:	School of Computing and Engineering
Keywords:	building energy simulation; building energy prediction; climate change; machine learning; weather morphing; EnergyPlus 23.2.0; XGBoost 2.1.1; climate stress-testing; non-stationarity; hotel energy performance; urban sustainability
URI:	https://repository.uwl.ac.uk/id/eprint/15298
Sustainable Development Goals:	Goal 7: Affordable and Clean Energy	Sustainable Development Goals:	Goal 9: Industry, Innovation, and Infrastructure	Sustainable Development Goals:	Goal 11: Sustainable Cities and Communities	Sustainable Development Goals:	Goal 13: Climate Action

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