Short-Term Power Prediction of Wind Power Generation System Based on Logistic Chaos Atom Search Optimization BP Neural Network

Abstract

Wind power generation is the major approach to wind energy utilization. However, due to the volatility, intermittent, and controllability of wind power, it is difficult to control and scheduling of wind power, which brings challenges to the grid-connected operation and dispatch of wind power. Therefore, accurate power prediction of the wind power generation system is worthy of in-depth study. And this paper proposes a wind power prediction model based on logistic chaos atom search optimization (LCASO) optimized back-propagation (BP) neural network, aiming to achieve accurate and efficient power prediction. Moreover, this work utilizes data preprocessing to obtain more precise prediction results and related prediction evaluation indexes to quantificationally compare the effect of the proposed one with other prediction models based on GA-BP neural network and PSO-BP neural network. In contrast with the BP neural network, GA-BP neural network, and PSO-BP neural network, the simulation tests verify the comprehensive prediction performance and wider applicability of LCASO-BP neural network-based power prediction model.

Failure prediction of mechanical system based on meta‐action

“…Scholars have proposed many improvement methods based on its shortcomings. [38][39][40] The gradient descent algorithm, as the basic correction algorithm of the BP neural network, is fast when adjusting the initial weight values but slow when approaching the optimal value. The Newton method is a fast optimization method based on the second-order Taylor series, which can respond quickly when approaching the optimal value.…”

Multilayer Perceptron Network Optimization for Chaotic Time Series Modeling

“…This characteristic of nonlinear dynamical systems is called chaos. Chaotic dynamical systems are ubiquitous in nature, so they have been extensively studied to solve practical problems in different fields, such as financial systems analysis [1,2], power system behavior [3,4], information security [5,6], and the control of nonlinear systems [7,8]. In general, chaotic dynamical systems do not have an explicit dynamical equation and can only be understood through the available time series.…”