A global model of hourly space heating and cooling demand at multiple spatial scales

Accurate modelling of the weather's temporal and spatial impacts on building energy demand is critical to decarbonizing energy systems. Here we introduce a customizable model for hourly heating and cooling demand applicable globally at all spatial scales. We validate against demand from similar to 5,000 buildings and 43 regions across four continents. The model requires limited data inputs and shows better agreement with measured demand than existing models. We use it first to demonstrate that a 1 degrees C reduction in thermostat settings across all buildings could reduce Europe's gas consumption by 240 TWh yr(-1), approximately one-sixth of historical imports from Russia. Second, we show that service demand for cooling is increasing by up to 5% per year in some regions due to climate change, and 5 billion people experience >100 additional cooling degree days per year when compared with a generation ago. The model and underlying data are freely accessible to promote further research.

Automated summary

Accurate modelling of weather impacts on energy demand is crucial for reducing carbon emissions. We have developed a customizable model that accurately predicts hourly heating and cooling demand at various spatial scales globally. Our research validates the model against data from thousands of buildings and regions across four continents, showing better agreement than existing models. The findings demonstrate the potential of reducing gas consumption through thermostat settings and highlight the increasing cooling demand due to climate change, affecting billions of people.