Abstract: Accurately identifying the parameters of the air-conditioning load model is an important basis for tapping its energy saving and demand response potential. Current studies mostly adopt offline identification methods with low accuracy. Therefore, a data-driven online identification method of model parameters for air-conditioning is proposed. Firstly, the framework of data-driven online identification for parameters of the air-conditioning load model is established. Secondly, based on the air-conditioning load model, the data-driven online identification mechanism and method are proposed. Among them, the data-driven online identification mechanism is designed as the parameter update discrimination mechanism based on event-driven significant changes in parameters, and the dynamic threshold setting mechanism for parameters based on fluctuation ranges of historical parameters.Under this mechanism, a fast online identification method is established through the particle swarm optimization algorithm.Finally, the effectiveness of the proposed online identification method is verified in a practical experimental environment.Compared with offline identification methods, the proposed method improves the calculation speed and accuracy more greatly, and can meet the needs of online applications.