New dataset RESCAST-100K aids cross-domain residential energy forecasting

Researchers have introduced RESCAST-100K, a large-scale dataset designed to improve cross-domain forecasting for residential energy load and indoor temperature. The dataset, generated using EnergyPlus simulations for approximately 100,000 U.S. homes, includes detailed time-series data and building covariates. It also integrates five real-world datasets to facilitate sim-to-real evaluation, aiming to advance machine learning applications in home and grid-scale energy management. AI

IMPACT Enables more accurate cross-domain forecasting for residential energy management and grid response.

RANK_REASON The cluster contains a research paper introducing a new dataset for machine learning tasks. [lever_c_demoted from research: ic=1 ai=1.0]

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