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Projected climate change will significantly influence the shape of the end-use energy demand profiles for space conditioning—leading to a likely increase in cooling needs and a subsequent decrease in heating needs. This shift will put pressure on existing infrastructure and utility companies to meet a demand that was not accounted for in the initial design of the systems. Furthermore, the traditional linear models typically used to predict energy demand focus on isolating either the electricity or natural gas demand, even though the two demands are highly interconnected. This practice often leads to less accurate predictions for both demand profiles. Here, we propose a multivariate, multi-sector (i.e., residential, commercial, industrial) framework to model the climate sensitivity of the coupled electricity and natural gas demand simultaneously, leveraging advanced statistical learning algorithms. Our results indicate that …