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, it is particularly important to reduce energy consumption and carbon emissions in the building sector. Chillers, as the main source of energy consumption in building equipment, will significantly exacerbate the building's energy consumption in the event of a failure. In order to improve the detection rate of early chiller failure, an improved golden jackal optimization algorithm (IGJO)-hybrid kernel extremum learning machine (HKELM) chiller fault diagnosis model is proposed. Firstly, to address the problem of slow convergence speed and easy to fall into local optimization of the golden jackal algorithm (GJO), we introduce four strategies, namely, improved sine chaotic mapping, simplex method, fusion of golden sinusoidal formula and Cauchy's variant, and put forward the IGJO algorithm to improve the algorithm's convergence speed and global optimization ability; and then, we use the fast optimization search of the hyperparameters combinations of the HKELM using the IGJO, to build a data-driven model for chiller failure detection; and finally, we use the data-driven model of ASEAN to construct a data-driven model of HKELM. The data-driven model is then used to construct a data-driven model for chiller fault detection; finally, the performance of the proposed model is simulated using ASHRAE 1043-RP. The results show that the accuracy of the proposed IGJO-HKELM model for chiller fault diagnosis reaches 99.83%, which is an obvious advantage compared with other algorithms.