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 the season-to-date heating and cooling degree-days, as well as the dew point temperature are the key predictors for both the electricity and natural gas demand. We also found that the energy sector is most sensitive to climate during the autumn and spring (intermediate) seasons, followed by the summer and winter seasons. Moreover, the proposed model outperforms a similar univariate model in terms of predictive accuracy, indicating the importance of accounting for the interdependence within the energy sectors. By providing accurate predictions of the electricity and natural gas demand, the proposed framework can help infrastructure planners and operators make informed decisions towards ensuring balanced energy delivery and minimizing supply inadequacy risks under future climate variability and change.

预计的气候变化将极大地影响用于空间调节的最终用途能源需求曲线的形状，从而导致制冷需求可能增加，供热需求随后减少。这种转变将给现有的基础设施和公用事业公司带来压力，以满足无法在系统初始设计中解决的需求。此外，通常用于预测能源需求的传统线性模型着重于隔离电力或天然气需求，即使这两个需求是高度互连的。这种做法通常会导致对两种需求状况的准确预测不足。在这里，我们提出了一个多变量，多部门（即住宅，商业，工业）框架，以同时对电力和天然气需求的气候敏感性建模，利用先进的统计学习算法。我们的结果表明，季节至今的供热和降温天数以及露点温度是电力和天然气需求的关键预测指标。我们还发现，能源部门在秋季和春季（中级）季节对气候最敏感，其次是夏季和冬季。此外，在预测准确性方面，所提出的模型优于类似的单变量模型，这表明考虑能源部门内部相互依赖性的重要性。通过提供电力和天然气需求的准确预测，