Towards climate-responsive demand modelling: quantifying the value of temperature and strategies to resolve the true dependency

Modelling daily electricity demand is an essential step to ensure grid stability and to meet society’s needs. Temperature is a key driver of demand, as it not only influences the seasonal variability but also the extremes. Day number is commonly used as a proxy for seasonality and is especially efficient at capturing the lower demand of the summer holiday. This is, however, a static feature and therefore not a sufficient choice when modelling demand in a changing climate. It is therefore of great interest to further investigate how to best resolve the true impact of temperature in demand models.

This study quantifies the gain in model performance when utilizing meteorological parameters directly versus using day number only. Furthermore, we evaluate feature engineering strategies to improve the model's ability to leverage the predictive information embedded in temperature. This was done using Generalized Additive Models (GAMs) to model the weather- and calendar-dependent daily electricity demand for nine European countries and assessing different feature combinations.

The results demonstrate an overall improvement in model performance when temperature is included in the modelling across all countries. The most significant improvements are seen in the Nordics and France, with up to 51.5% decrease in mean absolute error (MAE) compared to using day number alone. The significance of temperature is most pronounced when assessing model performance on the upper 5th percentile of daily demand, where the reduction in MAE is up to 69.0%. These findings underscore temperature’s critical role in capturing extreme demand events and highlight the need for climate-responsive modelling strategies.