Scenario-based prediction of climate change impacts on building cooling energy consumption with explainable artificial intelligence

In this paper, we present a newly developed eXplainable artificial intelligence (XAI) model to analyze the impacts of climate change on the cooling energy consumption (Ec) in buildings, predict long-term Ec under the new shared socioeconomic pathway (SSP) climate change scenarios, and explain the underlying reasons behind the predictions. Such analyses and future predictions are imperative to allow decision-makers and stakeholders to accomplish climate-resilient and sustainable development goals by leveraging the power of meaningful and trustworthy projections and insights. We demonstrated that the XAI is capable of predicting the Ec under future climate scenarios with high accuracy (R2>0.9) and reveals the critical inflection points of the daily average outdoor air temperature (Ta) beyond which the Ec increase exponentially. We applied the XAI model for residential and commercial buildings in hot–humid and mixed–humid climate regions to quantify the incremental impacts of climate change on Ec under the different SSPs. The XAI-based analysis concluded positive and persistent incremental changes in the Ec from 2020 to 2100 under all future SSP scenarios, with the maximum incremental impact of 24.5%, 33.3%, 57.8%, and 87.2% in hot–humid and 37.1%, 47.5%, 85.3%, and 121% in mixed–humid climate regions under the sustainable green energy (SSP126), business-as-usual (SSP245), challenges to adaptation (SSP370), and increased reliance on fossil fuels (SSP585) scenarios, respectively. Potential increases in the Ec in future climates could have significant adverse impacts on the local and regional economy if necessary adaptation and mitigation measures are not implemented a priori.