Predicting the impact of climate change on building energy consumption by using data-driven approaches

Buildings are complex thermodynamic entities that account for a large proportion of energy consumption. This work explores the application of data-driven models in order to forecast the building energy consumption over long time horizons. Long Short-Term Memory and Random Forest are used to forecast hourly heating and cooling energy consumption in the Urban Sciences Building, which is located in the city of Newcastle upon Tyne, UK. A synthetic time-series dataset is constructed using (a) a validated EnergyPlus model and (b) operational data hosted by Newcastle Urban Observatory. The energy consumption forecast is made using future climate data that represent a high emission future scenario during a typical year, that is 2030, and 2080. The experimental results suggest that, on average over the next six decades, there will be a 45% reduction in annual heating load, accompanied by a 680% increase in annual cooling load. These early findings indicate a notable shift in building thermal performance, a rise in overall building energy demand under the more drastic future weather scenarios, and a sharper emphasis on designing building envelopes and energy systems to include decarbonised cooling solutions.