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

By PulseAugur Editorial · [1 sources] ·

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]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

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

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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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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Jainam Dhruva, Yousaf Raza, A. B. Siddique, Simone Silvestri ·

    RESCAST-100K: A Comprehensive Dataset for Cross-Domain Residential Load and Indoor Temperature Forecasting

    arXiv:2606.02852v1 Announce Type: new Abstract: Accurate short-term forecasting of residential energy load and indoor temperature is essential for home energy management systems, grid-level demand response, and community energy efficiency efforts. Domain adaptation and transfer l…

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