Short-term load forecasting for smart grids based on NARX neural networks

Accurate load forecasting is of great significance for the planning and operation of smart grids. In order to further improve the accuracy of short-term load forecasting, an AGPSO-NARX load forecasting model is proposed, and the input information of the neural network is selected. Firstly, the particle swarm optimization algorithm is optimized by introducing variable weights, variable learning factors, and selection, crossover, and mutation operations, and the initial thresholds and weights of the NARX neural network are optimized based on this algorithm. Secondly, through experiments, suitable influencing factors are selected as inputs to the network to further improve the prediction accuracy. Finally, two scenarios, winter and summer, are considered to test the predictive ability of the neural network model through examples. The results show that the overall error and single-point error of the model are significantly reduced, indicating a high prediction accuracy.