## 1 Introduction
(2018) employed computational fluid dynamics (CFD) results of a gas turbine combustion chamber to train a fast prediction ANN model, which significantly reduces CFD calculation time while maintaining high prediction accuracy. [PERSON] et al. [PERSON] et al. (2018) employed a deep fully connected neural network to establish a mapping relationship between the state parameters of the PW100 engine and its thrust, fuel consumption rate, and exergy efficiency, which achieve high accuracy and can be used to optimize the energy and exergy performances of a F135 PW100 engine. It can be observed that the data-driven models are widely applicable in gas turbine modeling and can achieve high prediction accuracy. During the operation of gas turbines, due to factors such as load fluctuations and ambient temperature changes, the operating conditions and equipment characteristics will also undergo corresponding changes. Data-driven models based on a single basic model often struggle to maintain high accuracy across all operating conditions. Because the data quantity is not evenly distributed under different operating conditions, some conditions characteristics with few data points cannot be captured by basic models. The basic model faces challenges when seeking to accurately capture performance characteristics under all operating conditions. To enhance the predictive accuracy of the data-driven models under different operating conditions, some studies have considered using clustering algorithms to establish gas turbine models. [PERSON] et al. [PERSON] et al. (2018) represented the control parameters of a gas turbine using the fuzzy clustering method based on Gustafson-Kessel algorithms, demonstrating the potential application of clustering algorithms in modeling gas turbines. [PERSON] et al. [29] proposed a fuzzy modeling strategy that combines an entropy-based clustering algorithm with the subspace identification (SID) strategy to address the non-linearity of gas turbines under various conditions, resulting in a highly accurate model. Considering the rapid and accurate prediction abilities of data models, this paper establishes a gas turbine power prediction model based on machine learning algorithms. Recognizing the non-linearity of gas turbines and the limitations of a single model, a combined modeling approach is proposed. First, a basic model for gas turbine power prediction is conducted, providing an overall description of the data. Second, considering the performance variations of gas turbines under different operating conditions, a clustering algorithm is employed to categorize the data into distinct working modes. For each operation condition, a modification model is constructed, forming a set of such models to enhance local prediction accuracy. The main contributions of this paper are as follows:  
1. The integration of two-stage SVR and clustering introduces a composite framework that adapts dynamically to diverse operating conditions. First, a basic model is established to capture overall data characteristics. Then the operational data are categorized into different conditions through clustering. Finally, another SVR model is developed for each condition for model modification. 2. The finding that 70% of annual operational data are sufficient for optimal model performance provides valuable guidance for industrial implementation. This threshold represents a balance between data collection requirements and model accuracy, enabling faster deployment while maintaining prediction reliability. 3. This study establishes a combined model for a power prediction of an E-class gas turbine. To validate the performance of the combined model, the predictive performance of the basic model and the combined model are compared on the test set across multiple aspects, including Mean Absolute Percentage Error (MAPE), Maximum Absolute Percentage Error (MaxAPE), R-squared (R\({}^{2}\)), and error distribution. Specifically, the MAPE of the combined model is 0.2346%, compared to 0.3491% for the basic model, representing an improvement of 32.66%.