Owing to the vast and heterogeneous nature of power measurement data generated during power grid scheduling, integrating multisource heterogeneous data and constructing a big data warehouse for power grid scheduling error prevention present significant challenges. This study proposes a method for developing a big data warehouse for power grid scheduling error prevention through the fusion of multisource heterogeneous data. First, the Bidirectional Encoder Representations from Transformers and the Visual Geometry Group Network with 19 layersmodels were employed to extract comprehensive semantic and visual features from text and image data, respectively. Thereafter, a joint Kalman filter algorithm was implemented for feature fusion of the multisource heterogeneous data. Furthermore, an architecture for the power grid scheduling error prevention big data warehouse was developed to facilitate efficient management, rapid retrieval, and intelligent error prevention of power grid big data. Intelligent retrieval for power grid error prevention was achieved using the big data warehouse coupled with the Classification and Regression Tree algorithm. Finally, the effectiveness of the proposed method was validated through simulated experiments. The simulation results demonstrated that the proposed method achieved efficient management and rapid retrieval of power grid scheduling data, which effectively enhanced the level of intelligent error prevention in power grid scheduling.