Short-term Load Prediction of Integrated Energy System with Wavelet Neural Network Model Based on Improved Particle Swarm Optimization and Chaos Optimization Algorithm

To improve energy efficiency and protect the environment, the integrated energy system (IES) becomes a significant direction of energy structure adjustment. This paper inno-vatively proposes a wavelet neural network (WNN) model optimized by the improved particle swarm optimization (IPSO) and chaos o...

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Autores principales:	Leijiao Ge, Yuanliang Li, Jun Yan, Yuqian Wang, Na Zhang
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Integrated energy system (IES) load prediction chaos optimization algorithm (COA) improved particle swarm optimization (IPSO) Pearson correlation coefficient wavelet neural network (WNN) Production of electric energy or power. Powerplants. Central stations TK1001-1841 Renewable energy sources TJ807-830
Acceso en línea:	https://doaj.org/article/5aeed46b9323403a813cb04f189ca36b
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spelling	oai:doaj.org-article:5aeed46b9323403a813cb04f189ca36b2021-11-30T00:00:29ZShort-term Load Prediction of Integrated Energy System with Wavelet Neural Network Model Based on Improved Particle Swarm Optimization and Chaos Optimization Algorithm2196-542010.35833/MPCE.2020.000647https://doaj.org/article/5aeed46b9323403a813cb04f189ca36b2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9627862/https://doaj.org/toc/2196-5420To improve energy efficiency and protect the environment, the integrated energy system (IES) becomes a significant direction of energy structure adjustment. This paper inno-vatively proposes a wavelet neural network (WNN) model optimized by the improved particle swarm optimization (IPSO) and chaos optimization algorithm (COA) for short-term load prediction of IES. The proposed model overcomes the disadvantages of the slow convergence and the tendency to fall into the local optimum in traditional WNN models. First, the Pearson correlation coefficient is employed to select the key influencing factors of load prediction. Then, the traditional particle swarm optimization (PSO) is improved by the dynamic particle inertia weight. To jump out of the local optimum, the COA is employed to search for individual optimal particles in IPSO. In the iteration, the parameters of WNN are continually optimized by IPSO-COA. Meanwhile, the feedback link is added to the proposed model, where the output error is adopted to <sup>mo</sup>dify the prediction results. Finally, the proposed model is employed for load prediction. The experimental simulation verifies that the proposed model significantly improves the prediction accuracy and operation efficiency compared with the artificial neural network (ANN), WNN, and PSO-WNN.Leijiao GeYuanliang LiJun YanYuqian WangNa ZhangIEEEarticleIntegrated energy system (IES)load predictionchaos optimization algorithm (COA)improved particle swarm optimization (IPSO)Pearson correlation coefficientwavelet neural network (WNN)Production of electric energy or power. Powerplants. Central stationsTK1001-1841Renewable energy sourcesTJ807-830ENJournal of Modern Power Systems and Clean Energy, Vol 9, Iss 6, Pp 1490-1499 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Integrated energy system (IES) load prediction chaos optimization algorithm (COA) improved particle swarm optimization (IPSO) Pearson correlation coefficient wavelet neural network (WNN) Production of electric energy or power. Powerplants. Central stations TK1001-1841 Renewable energy sources TJ807-830
spellingShingle	Integrated energy system (IES) load prediction chaos optimization algorithm (COA) improved particle swarm optimization (IPSO) Pearson correlation coefficient wavelet neural network (WNN) Production of electric energy or power. Powerplants. Central stations TK1001-1841 Renewable energy sources TJ807-830 Leijiao Ge Yuanliang Li Jun Yan Yuqian Wang Na Zhang Short-term Load Prediction of Integrated Energy System with Wavelet Neural Network Model Based on Improved Particle Swarm Optimization and Chaos Optimization Algorithm
description	To improve energy efficiency and protect the environment, the integrated energy system (IES) becomes a significant direction of energy structure adjustment. This paper inno-vatively proposes a wavelet neural network (WNN) model optimized by the improved particle swarm optimization (IPSO) and chaos optimization algorithm (COA) for short-term load prediction of IES. The proposed model overcomes the disadvantages of the slow convergence and the tendency to fall into the local optimum in traditional WNN models. First, the Pearson correlation coefficient is employed to select the key influencing factors of load prediction. Then, the traditional particle swarm optimization (PSO) is improved by the dynamic particle inertia weight. To jump out of the local optimum, the COA is employed to search for individual optimal particles in IPSO. In the iteration, the parameters of WNN are continually optimized by IPSO-COA. Meanwhile, the feedback link is added to the proposed model, where the output error is adopted to <sup>mo</sup>dify the prediction results. Finally, the proposed model is employed for load prediction. The experimental simulation verifies that the proposed model significantly improves the prediction accuracy and operation efficiency compared with the artificial neural network (ANN), WNN, and PSO-WNN.
format	article
author	Leijiao Ge Yuanliang Li Jun Yan Yuqian Wang Na Zhang
author_facet	Leijiao Ge Yuanliang Li Jun Yan Yuqian Wang Na Zhang
author_sort	Leijiao Ge
title	Short-term Load Prediction of Integrated Energy System with Wavelet Neural Network Model Based on Improved Particle Swarm Optimization and Chaos Optimization Algorithm
title_short	Short-term Load Prediction of Integrated Energy System with Wavelet Neural Network Model Based on Improved Particle Swarm Optimization and Chaos Optimization Algorithm
title_full	Short-term Load Prediction of Integrated Energy System with Wavelet Neural Network Model Based on Improved Particle Swarm Optimization and Chaos Optimization Algorithm
title_fullStr	Short-term Load Prediction of Integrated Energy System with Wavelet Neural Network Model Based on Improved Particle Swarm Optimization and Chaos Optimization Algorithm
title_full_unstemmed	Short-term Load Prediction of Integrated Energy System with Wavelet Neural Network Model Based on Improved Particle Swarm Optimization and Chaos Optimization Algorithm
title_sort	short-term load prediction of integrated energy system with wavelet neural network model based on improved particle swarm optimization and chaos optimization algorithm
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/5aeed46b9323403a813cb04f189ca36b
work_keys_str_mv	AT leijiaoge shorttermloadpredictionofintegratedenergysystemwithwaveletneuralnetworkmodelbasedonimprovedparticleswarmoptimizationandchaosoptimizationalgorithm AT yuanliangli shorttermloadpredictionofintegratedenergysystemwithwaveletneuralnetworkmodelbasedonimprovedparticleswarmoptimizationandchaosoptimizationalgorithm AT junyan shorttermloadpredictionofintegratedenergysystemwithwaveletneuralnetworkmodelbasedonimprovedparticleswarmoptimizationandchaosoptimizationalgorithm AT yuqianwang shorttermloadpredictionofintegratedenergysystemwithwaveletneuralnetworkmodelbasedonimprovedparticleswarmoptimizationandchaosoptimizationalgorithm AT nazhang shorttermloadpredictionofintegratedenergysystemwithwaveletneuralnetworkmodelbasedonimprovedparticleswarmoptimizationandchaosoptimizationalgorithm
_version_	1718406865800396800

Short-term Load Prediction of Integrated Energy System with Wavelet Neural Network Model Based on Improved Particle Swarm Optimization and Chaos Optimization Algorithm

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