Short-Term Load Forecasting Model of Ameliorated CNN Based on Adaptive Mutation Fruit Fly Optimization Algorithm

In order to improve the accuracy and calculating speed of load forecasting for the strong nonlinear problem of short-term load, this article proposes a Short-term Load Forecasting Model of Ameliorated CNN Based on Adaptive Mutation Fruit Fly Optimization Algorithm. This method integrates the Extreme Learning Machine (ELM) algorithm into the Convolutional Neural Network (CNN): replace the fully connected layer in the original CNN network with ELM to form a CNN-ELM network. The purpose is to improve the calculation accuracy. An Adaptive Mutation Fruit Fly Optimization Algorithm (AMFOA) was proposed to reduce the probability that the Fruit Fly Optimization Algorithm (FOA) would easily fall into a local optimal value. And then AMFOA is used to optimize the parameters in CNN-ELM network. The above model is used to predict the grid load of a certain area in northern China. Compared with other prediction algorithms, it is proved that the model proposed in this article has higher prediction accuracy and also proved that the model has higher calculation speed than other models.