Elapsed-time-aware Liquid Neural Networks for direct coarse-interval indoor temperature forecasting in demand-responsive HVAC control

Accurate and flexible indoor temperature forecasts are essential for optimizing heating, ventilation, and air conditioning (HVAC) operation, reducing energy consumption, and participating in demand response programs. Fine-resolution forecasts can be post-aggregated when continuous 5-minute measurements and repeated short-interval inference are available, and this strategy is a strong accuracy baseline. This study instead evaluates a fine-to-coarse direct deployment setting in which a Liquid Neural Network (LNN) is calibrated on 5-minute building data and then evaluated directly at 30-minute and 1-hour supervisory-control intervals without retraining or repeated 5-minute inference for the coarse output. The proposed LNN uses elapsed-time-aware continuous-time state updates to rescale the hidden-state transition across these control intervals. Under the single-step direct coarse-interval inference setting, the LNN achieves up to 0.864 in R 2 and 0.017 in coefficient of variation of root mean square error (CV-RMSE), and reduces mean squared error (MSE) by up to 36 % compared with the Transformer-family baselines evaluated under the same direct-inference protocol. In the more demanding 12-step coarse-resolution setting, however, R 2 decreases substantially, indicating that direct long-horizon coarse-interval inference remains challenging; low CV-RMSE values in this setting should be interpreted cautiously because indoor temperature has a high mean and narrow variation range. These results should be interpreted separately from the rolling aggregation regime, where post-aggregated 5-minute forecasts provide the strongest point-accuracy baseline. Additional aggregation-aware comparisons show that direct LNN inference provides a lower-complexity deployment option and remains competitive under same-origin horizon-matched 1-hour evaluation. With only 8950 parameters, over three orders of magnitude fewer than typical attention models, the LNN enables fast, on-device inference suitable for edge HVAC controllers. Additional physical validation shows that the full-model response to an indoor-temperature perturbation has a fitted decay time constant of 14.80 min, close to the estimated air-dominant thermal time constant of 12.56 min. Closed-loop surrogate model predictive control (MPC) simulations further show that the LNN improves the comfort–energy tradeoff under coarser control resolutions, including an 11.54% nominal control-energy proxy reduction relative to Transformer at the 30-minute resolution under better below-setpoint comfort.