Aiming at the problem of the low accuracy of grid supply load forecasting in high permeability areas of small hydropower, a grid supply load forecasting model based on artificial intelligence and residual correction is proposed to predict and correct the periodic and random components contained in the grid supply load. Ensemble empirical modal decomposition (EEMD) is used to decompose and extract the components of different frequency bands in the network load, and a multi-level gated recurrent unit (GRU) network model based on modal components is constructed, the accuracy of the prediction results on the test set is improved by increasing the complexity of the network model. In addition, the Fischer value is introduced to characterize the cumulative effect of rainfall on the output of small hydropower, and the fisher information-weighted Markov Chain (FI-WMC) residual correction step is added in the output of prediction results, reduce the deviation of prediction result caused by the uncertainty of small hydropower output. The results of simulation verification show that the multi-level EEMD-GRU-FIWMC model can be better applied to the grid load forecasting in areas with high permeability of small hydropower. In areas where the penetration rate of small hydropower is above 20%, compared with the traditional GRU model and the no-residual correction model, its prediction accuracy is increased by 7.61% and 3.85%, respectively.