Application of chaos and neural network in power load forecasting
type: Item
instance of: scholarly article
title: Application of chaos and neural network in power load forecasting
zbMATH Open document ID: 1245.93100
DOI: 10.1155/2011/597634
author: Li Li
author: Chongxin Liu
published in: Discrete Dynamics in Nature and Society
publication date: 2012-01-23T00:00:00Z
review text: Summary: This paper employs chaos theory into power load forecasting. Lyapunov exponents on chaos theory are calculated to judge whether it is a chaotic system. Delay time and embedding dimension are calculated to reconstruct the phase space and determine the structure of Artificial Neural Network (ANN). Improved Back Propagation (BP) algorithm based on Genetic Algorithm (GA) is used to train and forecast. Finally, this paper uses the load data of Shaanxi province power grid of China to complete the short-term load forecasting. The results show that the model in this paper is more effective than classical standard BP neural network model.
Mathematics Subject Classification ID: 93C95
Mathematics Subject Classification ID: 93C15
Mathematics Subject Classification ID: 34H10
Mathematics Subject Classification ID: 92B20
zbMATH DE Number: 5999647
zbMATH Keywords: power load forecasting
zbMATH Keywords: chaotic systems
zbMATH Keywords: Lyapunov exponents
zbMATH Keywords: artificial neural network (ANN)
zbMATH Keywords: back propagation (BP) algorithm
zbMATH Keywords: genetic algorithm (GA)
Wikidata QID: Q58655520
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2011/597634
OpenAlex ID: W2022353411
cites work: Synchronization of unified chaotic system based on passive control
cites work: Practical method for determining the minimum embedding dimension of a scalar time series
cites work: Determining Lyapunov exponents from a time series
description: scientific article; zbMATH DE number 5999647