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arXiv Machine Learning By Ting-Yu Dai, Takuya Kurihana, Wing Yee Au, Hon Yung Wong

NeuralBES: A Differentiable, Control-Aware Emulator for Scalable Building Energy Modeling

Read the original on arXiv Machine Learning →

NeuralBES is a differentiable emulator that blends a physics‑based resistance–capacitance thermal model with a shared neural encoder to predict building energy use. By mapping building metadata to physically bounded model coefficients, it solves a scalar linear recurrence in log‑space and uses a predictor–corrector loop to handle thermostat nonlinearity while maintaining full‑horizon gradient flow. Trained on the ResStock dataset, NeuralBES accurately models heterogeneous buildings across climate zones, achieving physics‑valid predictions with fewer parameters than transformer and recurrent baselines and outperforming grey‑box RC models in mean absolute percentage error.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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