A Hybrid GA-PSO-CNN Model for Ultra-Short-Term Wind Power Forecasting

Accurate and timely wind power forecasting is essential for achieving large-scale wind power grid integration and ensuring the safe and stable operation of the power system. For overcoming the inaccuracy of wind power forecasting caused by randomness and volatility, this study proposes a hybrid convolutional neural network (CNN) model (GA-PSO-CNN) integrating genetic algorithm (GA) and a particle swarm optimization (PSO). The model can establish feature maps between factors affecting wind power such as wind speed, wind direction, and temperature. Moreover, a mix-encoding GA-PSO algorithm is introduced to optimize the network hyperparameters and weights collaboratively, which solves the problem of subjective determination of the optimal network in the CNN and effectively prevents local optimization in the training process. The prediction effectiveness of the proposed model is verified using data from a wind farm in Ningxia, China. The results show that the MAE, MSE, and MAPE of the proposed GA-PSO-CNN model decreased by 1.13-9.55%, 0.46-7.98%, and 3.28-19.29%, respectively, in different seasons, compared with Single-CNN, PSO-CNN, ISSO-CNN, and CHACNN models. The convolution kernel size and number in each convolution layer were reduced by 5-18.4% in the GA-PSO-CNN model.

Automated summary

Accurate wind power forecasting is crucial for wind power grid integration. The proposed GA-PSO-CNN model, which optimizes network structure and parameters, shows improved performance compared to other models, reducing error metrics and convolution kernel size.