An innovative method-based CEEMDAN-IGWO-GRU hybrid algorithm for short-term load forecasting

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 proposes a hybrid model which combines complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), gated recurrent unit (GRU) with improved gray wolf optimizer (IGWO), namely CEEMDAN-IGWO-GRU (CIG) hybrid algorithm. Firstly, the details and trend information of the load signal are separated by the CEEMDAN algorithm to suppress the interference of load fluctuation. 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. The power load data of a certain area under study is used to verify the CIG model, and the experimental results are compared with other existing algorithms. The experimental results show that the load forecasting results of the CIG model get the high-precision evaluation of 0.6997%, 52.4685, and 38.1891 MW in MAPE, RMSE, and MAE, respectively. Therefore, the parameter optimization ability of the IGWO algorithm can effectively improve the prediction accuracy, and the CIG method can availably suppress the impact of load fluctuation on prediction and has a powerful nonlinear fitting ability. In conclusion, CIG has great potential in establishing a power load forecasting model.