Power grid investment forecasting method based on PSO-GRU neural network

Traditional power grid investment forecasting models often ignore the cyclical characteristics of historical investment data, leading to one-sided investment allocation results and insufficient model generalization ability. This article proposed a power network investment forecasting method based on particle swarm optimization-gate recurrent unit (PSO-GRU) neural network. First, the temporal attention mechanism is introduced into the traditional GRU network, which improves the ability of the network to extract temporal features. Then, in order to avoid the adverse effects of unreasonable parameter configuration on model training, an optimized particle swarm optimization algorithm was proposed to optimize the parameter set of GRU and improve the training accuracy of the model. The data provided by an electric power company in China is used for experimental analysis, and the predicted results are compared with other electric power investment models. The RMSE of the PSO-GRU model proposed in this article is 0.1223, which is superior to other algorithms and has certain effectiveness.