Evaluating the climate sensitivity of coupled electricity-natural gas demand using a multivariate framework

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.