The accuracy level of Short-Term Load Forecasting (STLF) affects the power department's arrangements for unit start-up, shutdown, overhaul, and load dispatching. However, the existing algorithms do not fully consider load volatility and difficulty in setting the algorithm parameters. In this regard, this paper designs a CEEMDAN-IGWO-GRU (CIG) hybrid algorithm for STLF based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm for load stabilization, the powerful nonlinear fitting ability of Gated Recurrent Unit (GRU), and the parameter optimization ability of Improved Gray Wolf Optimizer (IGWO). Firstly, the details and trend information of the load signal are separated by the CEEMDAN algorithm. Then, the GRU network optimized by IGWO parameters is used to predict each component, separately. Finally, the complete load forecasting results are obtained by reconstructing the forecasting result of each component. To verify the validity of the CIG hybrid algorithm, experiments are performed based on power load data of a certain area under study, and the experimental results are compared with other existing algorithms. The experimental results show that the CIG hybrid algorithm performs well for STLF, and the accuracy indexes of the statistical standards are improved.