发表机构
Fujitsu Research of America（富士通美国研究院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
NeuralBES通过共享神经编码器参数化RC热模型，实现可微分、控制感知的建筑能耗仿真，在保持物理有效性的同时，以更少参数达到与最强基线相差4个MAPE点以内的精度。
AI 中文摘要
需求侧灵活性，即预测、转移和削减住宅能源负荷，依赖于在数百万异构建筑中值得信赖的热模型。现有工具面临艰难取舍：诸如EnergyPlus等高保真物理模拟器虽然精确，但需顺序执行并要求逐栋建筑校准；而纯数据驱动的序列模型虽可扩展，却放弃了使其预测可信的物理结构。我们提出NeuralBES（建筑能耗仿真），一种可微分仿真器，通过将基于电阻-电容（RC）的热模型与共享神经编码器参数化来解决这一取舍：静态建筑元数据（如建筑面积、年代和暖通空调类型）被映射到物理有界的电容、电导和设备系数，这些系数成为通过对数空间并行扫描求解的标量线性递推的系数，同时预测-校正回路闭合恒温器-温度非线性，并保留全时域梯度流。在跨三个气候区的ResStock数据集上训练，NeuralBES在单个训练编码器内处理异构建筑原型、年代和气候区，而黑盒基线产生统计上合理但物理上不一致的轨迹。在年度全年滚动测试中，NeuralBES是唯一同时满足物理有效且精度在最强原始误差基线4个MAPE点以内的数据条件模型，同时其参数量比Transformer和循环基线少约一个数量级；在参数等量的物理有效基线中，其MAPE比灰盒RC替代方案降低一半以上。
英文摘要
Demand-side flexibility i.e. forecasting, shifting, and curtailing residential energy loads, depends on thermal models trusted across millions of heterogeneous buildings. Existing tools force a hard tradeoff: high-fidelity physics simulators such as EnergyPlus are accurate but sequential and require per-building calibration, while purely data-driven sequence models scale but abandon the physical structure that makes their predictions trustworthy. We introduce NeuralBES (Building Energy Simulation), a differentiable emulator that resolves this tradeoff by parameterizing a resistance--capacitance (RC) based thermal model with a shared neural encoder: static building metadata such as floor area, vintage, and HVAC type is mapped to physically bounded capacitances, conductances, and equipment coefficients, which become the coefficients of a scalar linear recurrence solved via a log-space parallel scan, and a predictor--corrector loop closes the thermostat--temperature nonlinearity while preserving full-horizon gradient flow. Trained on the ResStock dataset across three climate zones, NeuralBES handles heterogeneous building archetypes, vintages, and climate zones within a single trained encoder, while black-box baselines produce statistically plausible but physically inconsistent trajectories. On the annual full-year rollout, NeuralBES is the only data-conditioned model that is simultaneously physics-valid and accurate to within 4 MAPE points of the strongest raw-error baseline, while operating at roughly an order of magnitude fewer parameters than the transformer and recurrent baselines; among physics-valid baselines at parameter parity it more than halves the MAPE of the grey-box RC alternative.
