The dependence of wind power on the natural environment leads to volatility, which can cause hidden dangers to the safe and stable operation of the power grid.In this work, a parts deep learning-based GoogLeNet-embedded no-pooling dimension fully-connected prediction network is proposed for the short-term prediction issue of wind power generation, and the deep learning-based GoogLeNet-embedded no-pooling dimension fully-connected network is compared with five algorithms including long short-term memory network and NasNet.The dataset was collected in Natal.The six algorithms employed predicted the value of wind power for the torpedo coming day.
Among all, the deep learning-based GoogLeNet embedded no-pooling dimension fully-connected network achieved the optimal prediction results and evaluation metrics.The percentage reduction of each metric value from the second smallest long short-term memory network for the deep learning-based GoogLeNet-embedded no-pooling dimension fully-connected network is 27.0% for mean absolute error, 27.2% for mean absolute percentage error, 34.
8% for mean squared error, 19.9% for root mean square error and 21.6% for symmetric mean absolute percentage error.