The operation of integrated energy systems (IESs) is confronted with great challenges for increasing penetration rate of renewable energy and growing complexity of energy forms. Scenario generation is one of ordinary methods to alleviate the system uncertainties by extracting several typical scenarios to represent the original high-dimensional data. This paper proposes a novel representative scenario generation method based on the feature extraction of panel data. The original high-dimensional data are represented by an aggregated indicator matrix using principal component analysis to preserve temporal variation. Then, the aggregated indicator matrix is clustered by an algorithm combining density canopy and K-medoids. Together with the proposed scenario generation method, an optimal operation model of IES is established, where the objective is to minimize the annual operation costs considering carbon trading cost. Finally, case studies based on the data of Aachen, Germany in 2019 are performed. The results indicate that the adjusted rand index (ARI) and silhouette coefficient (SC) of the proposed method are 0.6153 and 0.6770, respectively, both higher than the traditional methods, namely K-medoids, K-means++, and density-based spatial clustering of applications with noise (DBSCAN), which means the proposed method has better accuracy. The error between optimal operation results of the IES obtained by the proposed method and all-year time series benchmark value is 0.1%, while the calculation time is reduced from 11029 s to 188 s, which verifies that the proposed method can be used to optimize operation strategy of IES with high efficiency without loss of accuracy.