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

新数据集RESCAST-100K助力跨领域住宅能源预测

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-03 04:00

研究人员推出了RESCAST-100K，这是一个大规模数据集，旨在改进住宅能源负荷和室内温度的跨领域预测。该数据集使用EnergyPlus模拟生成，涵盖约10万户美国家庭，包含详细的时间序列数据和建筑协变量。它还集成了五个真实世界的数据集，以促进模拟到现实的评估，旨在推进机器学习在家庭和电网规模能源管理中的应用。 AI

影响能够实现更准确的住宅能源管理和电网响应的跨领域预测。

排序理由该集群包含一篇介绍用于机器学习任务的新数据集的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

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Signal score: 0 / 100; Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket: Tool; 该集群包含一篇介绍用于机器学习任务的新数据集的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Jainam Dhruva, Yousaf Raza, A. B. Siddique, Simone Silvestri · 2026-06-03 04:00

RESCAST-100K：用于跨领域住宅负荷和室内温度预测的综合数据集

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…

本文如何被排名

报道来源 [1]

RESCAST-100K：用于跨领域住宅负荷和室内温度预测的综合数据集

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