@article{Wang2026, 
author = {Ran Wang and Sijin Chen and Xin Zhao and Yuge Ma and Houze Jiang and Hongcheng Zhu and Shilei Lu and Yongjun Sun},
title = {A high-precision aggregate power prediction for air conditioner clusters using improved state-queuing model},
year = {2026},
journal = {Building Simulation},
volume = {19},
number = {6},
pages = {1625-1645},
keywords = {aggregate power, second-order equivalent thermal parameter model, state-queuing model, k-means clustering, compressor lock protection mechanism},
url = {https://www.sciopen.com/article/10.1007/s12273-026-1432-1},
doi = {10.1007/s12273-026-1432-1},
abstract = {Decentralized air conditioners offer significant potential for electricity market regulation. However, the compressor lock mechanism, often overlooked by existing models, causes units to reject switching commands, leading to prediction errors. To address this, this study proposes a high-precision aggregate power prediction method based on an improved state-queuing model. The framework integrates a second-order equivalent thermal parameter model with k-means clustering and introduces an explicit timer to accurately characterize delayed restart behaviors caused by compressor lock protection mechanism. Validation using GridLAB-D simulations covers regular operation and varying demand response events (0.5 h, 1 h, 2 h). Results demonstrate the method’s superiority. In regular operation, it maintains a mean relative error of 3.37%, compared to 49.3% for traditional methods. Under demand response, mean relative error remains about 2%, significantly lower than the 28%–38% of traditional approaches. Furthermore, it mitigates peak deviations by over 63% (keeping maximum deviation below 140 kW) and achieves a root mean square error of 27 kW. The method also exhibits strong adaptability to compressor lock times of 3–7 min, maintaining errors within 3.5%. These findings confirm that incorporating the lock mechanism is critical for precise aggregate control and efficient demand response integration.}
}