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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. [ABSTRACT FROM AUTHOR]

Aiming at the problem of unnecessary waste of power resources in the power system caused by the uncertainty of wind farm output, and then affecting the economics of the system. This paper proposes a power system scheduling model based on wind power output forecasting errors. In the forecasting stage, in view of the defect that the Fruit Fly Optimization Algorithm (FOA) is easy to fall into the local optimal value, an Adaptive Mutation Fruit Fly Optimization Algorithm (AMFOA) based on the flavor concentration variance is proposed. And then, the parameters of Extreme Learning Machine (ELM) are optimized by AMFOA to predict the wind power output. In the scheduling phase, a limit scenario is established based on the obtained prediction error to reduce the fluctuation of the wind farm output. When modeling, the influence of the prediction error is considered in the power balance equation, and it is constructed to maximize the probability of its establishment. And then, incorporate it into the objective function. Based on this, a scheduling model is established and solved by AMFOA. Finally, an example is used to verify the accuracy of the proposed forecasting model and the economics of the scheduling model. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]