对比了不同输入条件下住宅能耗预测模型的精度，发现真实场景下数据受限会显著影响效果。
Full-Feature versus Limited-Input Machine Learning for Residential Energy Estimation: A Comparative Analysis of RECS and ResStock Under Realistic Input Constraints
- 用树模型在全量特征和十项易得特征下对比预测性能
- 十项输入时模型R2达0.61~0.62，物理信息缺失导致性能下降
- 针对同质化群体可提升至R2=0.85，适合精准建模场景
住宅能耗估算常需在缺乏详细围护结构、设备效率、渗透率或计量数据前提下进行。本研究基于美国全国代表性数据集RECS（调查数据）与ResStock（模拟数据），量化预测精度与输入可得性之间的权衡。首先使用全特征模型建立基准性能：在全特征下，CatBoost表现最优，ResStock R²达0.90，RECS为0.73。随后将输入限制为十项可通过住户、行政记录或气象数据获取的低负担变量，模拟真实部署条件，此时模型性能收敛至RECS R²=0.61，ResStock R²=0.62，表明算法复杂度无法弥补物理与行为信息缺失。但在更同质化的ResStock子集（单户独立住宅、天然气供暖、气候区6A、2000–2010年建造）中，简化模型准确率提升至R²=0.85，证明针对性建模对均质群体有效。结果表明，树模型可作为国家级住宅能耗数据的高保真代理，但需综合考虑特征可得性、数据来源（实测/仿真）及应用场景。
原文摘要 · Abstract (English)
Residential energy estimates are often needed before detailed envelope characteristics, equipment efficiencies, infiltration, sensor, or billing data are available. This study quantifies the trade-off between predictive accuracy and input accessibility using two nationally representative U.S. residential-energy datasets: the survey-based Residential Energy Consumption Survey (RECS) and the simulation-based ResStock dataset. Full-feature models were first used to establish dataset-specific performance benchmarks. For total-energy estimation, the models were subsequently restricted to ten low-burden variables obtainable from occupants, administrative records, or location-based weather data without an on-site energy audit. Among CatBoost, XGBoost, LightGBM, Random Forest, and Neural Networks, CatBoost consistently achieved the highest predictive performance for the full-feature analysis, reaching R2 = 0.90 for ResStock and R2 = 0.73 for RECS. When the feature set was restricted to ten homeowner-accessible inputs to simulate realistic deployment conditions, model performance converged to R2 = 0.61 for RECS and R2 = 0.62 for ResStock, showing that algorithmic complexity cannot fully compensate for missing physical and behavioral information. However, for a more homogeneous ResStock cohort consisting of single-family detached, natural-gas-heated homes in Climate Zone 6A constructed between 2000 and 2010, a reduced-input model improved accuracy to R2 = 0.85, demonstrating the value of targeted modeling for homogeneous populations. The results indicate that tree-based ensemble models can serve as high-fidelity emulators of national-scale residential energy datasets. However, careful consideration of feature availability, dataset origin (empirical vs. synthetic), and applicable use cases are also important.
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