Adaptive Temperature Control of Air Conditioners Based on Millimeter-Wave Radar and Light Gradient Boosting Machine Model

To address the issues of large temperature fluctuations, poor spatial perception, and low
control robustness in traditional residential air conditioners, this paper proposes an adaptive
temperature control algorithm based on millimeter-wave radar and a Light Gradient Boosting
Machine (LGBM). Given that the bed is the primary obstacle and heat source in a bedroom,
we develop a bed localization method using point cloud clustering. This method accurately
identifies the bed position through time – window filtering, outlier removal, and density clustering.
An LGBM weak teacher model, trained on massive cloud data, takes the bed position, indoor
temperature, and compressor parameters as inputs to optimize air direction and fan speed, thereby
effectively suppressing steady-state fluctuations in the return air temperature. Experiments on
719 real – world devices demonstrate that the bed positioning accuracy reaches 83.6% under an
error tolerance of 0.5 m, the average absolute temperature fluctuation is reduced to 0.21◦C, and
the control accuracy of the air guide mechanism exceeds 0.98. The proposed method requires
no hardware modification, offers strong generalizability and low deployment cost, significantly
improves temperature stability and thermal comfort in bedroom environments, and provides a
feasible technical solution for intelligent residential air conditioning control.