Affiliation:
1. State Grid Information and Telecommunication Co ., Taiyuan Xinyuan Road, Shanxi 030021, China
Abstract
Abstract
With the development of smart grid, the demand for new energy power increases. Improving the accuracy of new energy power demand forecast is an important basis for the orderly operation of power system. This article presents a new energy power demand forecasting method based on DESSA-NESN algorithm. First, differential evolution algorithm (DE) and sparrow search algorithm (SSA) are combined, and operations such as mutation, crossing and screening are introduced into the population updating process of SSA. The internal state function of the savings pool of the standard echo state network (ESN) is replaced by the hyperbolic tangent function to obtain the nonlinear echo state network (NESN). Then, the parameters of deep echo state network (DESN) are optimized using DESSA algorithm. The DESSA-DESN prediction model is established. Finally, the mean absolute percentage error (MAPE) and root mean square error (RMSE) of DESSA-NESN were 15.84 and 0.12%, respectively, and the prediction effect was better than other comparison models.
Publisher
Oxford University Press (OUP)
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