An improved golden jackal optimization-hybrid kernel extreme learning machine approach for fault diagnosis of chillers

Abstract In the context of the dual carbon target, reducing energy consumption and carbon emissions in the building sector is crucial. Chillers, being the primary source of energy consumption in building equipment, can significantly increase energy use if they fail. To enhance the detection rate of early chiller failures, this paper proposes an improved chiller fault diagnosis model based on the golden jackal optimization algorithm (IGJO) and the hybrid kernel extremum learning machine (HKELM). To address the slow convergence speed and local optimization issues of the original golden jackal algorithm (GJO), we introduce four strategies: improved sine chaotic mapping, the simplex method, a fusion of the golden sinusoidal formula, and Cauchy’s variant. These strategies form the IGJO algorithm, which improves convergence speed and global optimization ability. Next, the IGJO algorithm is used to optimize the hyperparameters of the HKELM for fast search and efficient chiller fault detection. Finally, we simulate the performance of the proposed model using ASHRAE RP-1043. The results show that the IGJO-HKELM model achieves a chiller fault diagnosis accuracy of 99.83%, demonstrating a clear advantage over other algorithms.