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

Accurate short-term forecasting of residential energy load and indoor temperature is essential for home energy management systems, grid-level demand response participation, and community-level energy efficiency programs. Domain adaptation methods and transfer learning have shown promise in improving forecasting accuracy under data heterogeneity and scarcity scenarios commonly observed at the residential level. However, progress remains limited by the absence of comprehensive residential datasets: existing ones are narrow in target coverage and lack structured support for cross-domain evaluation. To address this, we introduce RESCAST-100K, a large-scale residential forecasting benchmark for studying cross-domain generalization. It exposes a configuration-driven interface that instantiates source and target domains along interpretable axes, such as geography, climate zone, wall construction, and heating equipment, supporting systematic evaluation of transfer learning, domain adaptation, and zero-shot generalization under controlled domain shifts. The benchmark covers approximately 100,000 EnergyPlus-simulated U.S. homes derived from ResStock, providing 15-minute time series of three coupled targets per home, namely total load, HVAC (heating, ventilation, and air conditioning) load, and indoor temperature, together with weather channels, HVAC setpoints, and over 40 static building covariates. Finally, it integrates five real-world residential datasets under a unified schema, supporting sim-to-real evaluation on the same tasks. We benchmark models from recurrent, attention, and MLP-mixer architectures for zero-shot performance across different domains, under missing input data conditions, for each task. Cross-attention and MLP-mixer based models consistently outperform recurrent and classical transformer-based baselines under domain shift. RESCAST-100K is intended to support the machine learning and building analytics communities in advancing cross-domain residential forecasting at the home, community, and grid levels.