Power Load Forecasting of General Regression Neural Network Based on Particle Swarm Optimization

To study the power load forecasting optimization of general regression neural network using particle swarm optimization. Refer to the domestic and foreign literature, The paper refers to domestic and foreign literature, by comparing the GRNN with BP, MDE with DE based on particle swarm optimization,...

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Main Author: Wei Wang
Format: Article
Language:English
Published: AIDIC Servizi S.r.l. 2018-07-01
Series:Chemical Engineering Transactions
Online Access:https://www.cetjournal.it/index.php/cet/article/view/2881
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author Wei Wang
author_facet Wei Wang
author_sort Wei Wang
collection DOAJ
description To study the power load forecasting optimization of general regression neural network using particle swarm optimization. Refer to the domestic and foreign literature, The paper refers to domestic and foreign literature, by comparing the GRNN with BP, MDE with DE based on particle swarm optimization, it compares the test result and studies the power load forecasting of the general regression neural network. The maximum error rate of the particle swarm optimization is lower than 9%, and the minimum error rate is approximately 0, the forecasting accuracy rate of general regression neural network is high. the optimization method has good convergence and is highly accurate, and could be applied to short-term power load forecasting.
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spelling doaj.art-49fff8a91ef64a6b92e436936860e69f2022-12-21T17:17:18ZengAIDIC Servizi S.r.l.Chemical Engineering Transactions2283-92162018-07-016610.3303/CET1866223Power Load Forecasting of General Regression Neural Network Based on Particle Swarm OptimizationWei WangTo study the power load forecasting optimization of general regression neural network using particle swarm optimization. Refer to the domestic and foreign literature, The paper refers to domestic and foreign literature, by comparing the GRNN with BP, MDE with DE based on particle swarm optimization, it compares the test result and studies the power load forecasting of the general regression neural network. The maximum error rate of the particle swarm optimization is lower than 9%, and the minimum error rate is approximately 0, the forecasting accuracy rate of general regression neural network is high. the optimization method has good convergence and is highly accurate, and could be applied to short-term power load forecasting.https://www.cetjournal.it/index.php/cet/article/view/2881
spellingShingle Wei Wang
Power Load Forecasting of General Regression Neural Network Based on Particle Swarm Optimization
Chemical Engineering Transactions
title Power Load Forecasting of General Regression Neural Network Based on Particle Swarm Optimization
title_full Power Load Forecasting of General Regression Neural Network Based on Particle Swarm Optimization
title_fullStr Power Load Forecasting of General Regression Neural Network Based on Particle Swarm Optimization
title_full_unstemmed Power Load Forecasting of General Regression Neural Network Based on Particle Swarm Optimization
title_short Power Load Forecasting of General Regression Neural Network Based on Particle Swarm Optimization
title_sort power load forecasting of general regression neural network based on particle swarm optimization
url https://www.cetjournal.it/index.php/cet/article/view/2881
work_keys_str_mv AT weiwang powerloadforecastingofgeneralregressionneuralnetworkbasedonparticleswarmoptimization