Short Term Power Load Forecasting Based on PSVMD-CGA Model

Short-term power load forecasting is critical for ensuring power system stability. A new algorithm that combines CNN, GRU, and an attention mechanism with the Sparrow algorithm to optimize variational mode decomposition (PSVMD-CGA) is proposed to address the problem of the effect of random load fluctuations on the accuracy of short-term load forecasting. To avoid manual selection of VMD parameters, the Sparrow algorithm is adopted to optimize VMD by decomposing short-term power load data into multiple subsequences, thus significantly reducing the volatility of load data. Subsequently, the CNN (Convolution Neural Network) is introduced to address the fact that the GRU (Gated Recurrent Unit) is difficult to use to extract high-dimensional power load features. Finally, the attention mechanism is selected to address the fact that when the data sequence is too long, important information cannot be weighted highly. On the basis of the original GRU model, the PSVMD-CGA model suggested in this paper has been considerably enhanced. MAE has dropped by 288.8%, MAPE has dropped by 3.46%, RMSE has dropped by 326.1 MW, and R2 has risen to 0.99. At the same time, various evaluation indicators show that the PSVMD-CGA model outperforms the SSA-VMD-CGA and GA-VMD-CGA models.

Automated summary

A new algorithm, PSVMD-CGA, is proposed in this paper to optimize variational mode decomposition (VMD) by combining CNN, GRU, and an attention mechanism with the Sparrow algorithm. The PSVMD-CGA model has been significantly enhanced compared to the original GRU model, with a decrease in MAE by 288.8%, MAPE by 3.46%, RMSE by 326.1 MW, and an increase in R2 to 0.99. Various evaluation indicators show that the PSVMD-CGA model outperforms the SSA-VMD-CGA and GA-VMD-CGA models.