NeuralBES: A Differentiable, Control-Aware Emulator for Scalable Building Energy Modeling
NeuralBES creates a differentiable emulator that balances accurate physics with scalable data-driven modeling for building energy forecasting. It parameterizes an RC thermal model using a shared neural encoder to map static building features to physical parameters, then solves the resulting linear recurrence in parallel. A predictor-corrector loop closes the thermostat-temperature nonlinearity, achieving high accuracy comparable to complex simulators while remaining much smaller.