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Short-term forecasting of residential and commercial building energyconsumption is widely used in power systems and continues to grow inimportance. Data-driven short-term load forecasting (STLF), although promising,has suffered from a lack of open, large-scale datasets with high buildingdiversity. This has hindered exploring the pretrain-then-fine-tune paradigm forSTLF. To help address this, we present BuildingsBench, which consists of: 1)Buildings-900K, a large-scale dataset of 900K simulated buildings representingthe U.S. building stock; and 2) an evaluation platform with over 1,900 realresidential and commercial buildings from 7 open datasets. BuildingsBenchbenchmarks two under-explored tasks: zero-shot STLF, where a pretrained modelis evaluated on unseen buildings without fine-tuning, and transfer learning,where a pretrained model is fine-tuned on a target building. The main findingof our benchmark analysis is that synthetically pretrained models generalizesurprisingly well to real commercial buildings. An exploration of the effect ofincreasing dataset size and diversity on zero-shot commercial buildingperformance reveals a power-law with diminishing returns. We also show thatfine-tuning pretrained models on real commercial and residential buildingsimproves performance for a majority of target buildings. We hope thatBuildingsBench encourages and facilitates future research on generalizableSTLF. All datasets and code can be accessed fromhttps://github.com/NREL/BuildingsBench.

BuildingsBench: A Large-Scale Dataset of 900K Buildings and Benchmark for Short-Term Load Forecasting