Our datasets and code are available via the following links: Github: https://github.com/NREL/BuildingsBench As described in Sec. 3 and Sec. 4, Buildings-900K and the BuildingsBench benchmark datasets are B.1 Motivation Q: For what purpose was the dataset created? It specifically addresses a lack of appropriately sized and diverse datasets for pretraining STLF models. We emphasize that the EULP was not originally developed for studying STLF. Rather, it was developed as a general resource to "...help electric utilities, grid operators, manufacturers, Q: Who created the dataset (e.g., which team, research group) and on behalf of which entity Q: Who funded the creation of the dataset?

In this section we provide full descriptions of the diffusion processes introduced in Sec. 3 .

Diffusion models beat gans on image synthesis.

Our experiments utilize contributions from existing code repositories.

In the top left, we have the distribution of the number of frames in each video.

Scores are calculated by giving a weight of 1 for applicable, 0.5 for conditionally applicable, and 0 for incorrect responses.