Multi-Objective Particle Swarm Optimization Algorithm for Multi-Step Electric Load Forecasting

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Zeitschriftentitel:	Energies
Personen und Körperschaften:	Yang, Yi, Shang, Zhihao, Chen, Yao, Chen, Yanhua
In:	Energies, 13, 2020, 3, S. 532
Format:	E-Article
Sprache:	Englisch
veröffentlicht:	MDPI AG
Schlagwörter:	Energy (miscellaneous) Energy Engineering and Power Technology Renewable Energy, Sustainability and the Environment Electrical and Electronic Engineering Control and Optimization Engineering (miscellaneous)

author_facet	Yang, Yi Shang, Zhihao Chen, Yao Chen, Yanhua Yang, Yi Shang, Zhihao Chen, Yao Chen, Yanhua
author	Yang, Yi Shang, Zhihao Chen, Yao Chen, Yanhua
spellingShingle	Yang, Yi Shang, Zhihao Chen, Yao Chen, Yanhua Energies Multi-Objective Particle Swarm Optimization Algorithm for Multi-Step Electric Load Forecasting Energy (miscellaneous) Energy Engineering and Power Technology Renewable Energy, Sustainability and the Environment Electrical and Electronic Engineering Control and Optimization Engineering (miscellaneous)
author_sort	yang, yi
spelling	Yang, Yi Shang, Zhihao Chen, Yao Chen, Yanhua 1996-1073 MDPI AG Energy (miscellaneous) Energy Engineering and Power Technology Renewable Energy, Sustainability and the Environment Electrical and Electronic Engineering Control and Optimization Engineering (miscellaneous) http://dx.doi.org/10.3390/en13030532 <jats:p>As energy saving becomes more and more popular, electric load forecasting has played a more and more crucial role in power management systems in the last few years. Because of the real-time characteristic of electricity and the uncertainty change of an electric load, realizing the accuracy and stability of electric load forecasting is a challenging task. Many predecessors have obtained the expected forecasting results by various methods. Considering the stability of time series prediction, a novel combined electric load forecasting, which based on extreme learning machine (ELM), recurrent neural network (RNN), and support vector machines (SVMs), was proposed. The combined model first uses three neural networks to forecast the electric load data separately considering that the single model has inevitable disadvantages, the combined model applies the multi-objective particle swarm optimization algorithm (MOPSO) to optimize the parameters. In order to verify the capacity of the proposed combined model, 1-step, 2-step, and 3-step are used to forecast the electric load data of three Australian states, including New South Wales, Queensland, and Victoria. The experimental results intuitively indicate that for these three datasets, the combined model outperforms all three individual models used for comparison, which demonstrates its superior capability in terms of accuracy and stability.</jats:p> Multi-Objective Particle Swarm Optimization Algorithm for Multi-Step Electric Load Forecasting Energies
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title	Multi-Objective Particle Swarm Optimization Algorithm for Multi-Step Electric Load Forecasting
title_unstemmed	Multi-Objective Particle Swarm Optimization Algorithm for Multi-Step Electric Load Forecasting
title_full	Multi-Objective Particle Swarm Optimization Algorithm for Multi-Step Electric Load Forecasting
title_fullStr	Multi-Objective Particle Swarm Optimization Algorithm for Multi-Step Electric Load Forecasting
title_full_unstemmed	Multi-Objective Particle Swarm Optimization Algorithm for Multi-Step Electric Load Forecasting
title_short	Multi-Objective Particle Swarm Optimization Algorithm for Multi-Step Electric Load Forecasting
title_sort	multi-objective particle swarm optimization algorithm for multi-step electric load forecasting
topic	Energy (miscellaneous) Energy Engineering and Power Technology Renewable Energy, Sustainability and the Environment Electrical and Electronic Engineering Control and Optimization Engineering (miscellaneous)
url	http://dx.doi.org/10.3390/en13030532
publishDate	2020
physical	532
description	<jats:p>As energy saving becomes more and more popular, electric load forecasting has played a more and more crucial role in power management systems in the last few years. Because of the real-time characteristic of electricity and the uncertainty change of an electric load, realizing the accuracy and stability of electric load forecasting is a challenging task. Many predecessors have obtained the expected forecasting results by various methods. Considering the stability of time series prediction, a novel combined electric load forecasting, which based on extreme learning machine (ELM), recurrent neural network (RNN), and support vector machines (SVMs), was proposed. The combined model first uses three neural networks to forecast the electric load data separately considering that the single model has inevitable disadvantages, the combined model applies the multi-objective particle swarm optimization algorithm (MOPSO) to optimize the parameters. In order to verify the capacity of the proposed combined model, 1-step, 2-step, and 3-step are used to forecast the electric load data of three Australian states, including New South Wales, Queensland, and Victoria. The experimental results intuitively indicate that for these three datasets, the combined model outperforms all three individual models used for comparison, which demonstrates its superior capability in terms of accuracy and stability.</jats:p>
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author	Yang, Yi, Shang, Zhihao, Chen, Yao, Chen, Yanhua
author_facet	Yang, Yi, Shang, Zhihao, Chen, Yao, Chen, Yanhua, Yang, Yi, Shang, Zhihao, Chen, Yao, Chen, Yanhua
author_sort	yang, yi
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container_title	Energies
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description	<jats:p>As energy saving becomes more and more popular, electric load forecasting has played a more and more crucial role in power management systems in the last few years. Because of the real-time characteristic of electricity and the uncertainty change of an electric load, realizing the accuracy and stability of electric load forecasting is a challenging task. Many predecessors have obtained the expected forecasting results by various methods. Considering the stability of time series prediction, a novel combined electric load forecasting, which based on extreme learning machine (ELM), recurrent neural network (RNN), and support vector machines (SVMs), was proposed. The combined model first uses three neural networks to forecast the electric load data separately considering that the single model has inevitable disadvantages, the combined model applies the multi-objective particle swarm optimization algorithm (MOPSO) to optimize the parameters. In order to verify the capacity of the proposed combined model, 1-step, 2-step, and 3-step are used to forecast the electric load data of three Australian states, including New South Wales, Queensland, and Victoria. The experimental results intuitively indicate that for these three datasets, the combined model outperforms all three individual models used for comparison, which demonstrates its superior capability in terms of accuracy and stability.</jats:p>
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spelling	Yang, Yi Shang, Zhihao Chen, Yao Chen, Yanhua 1996-1073 MDPI AG Energy (miscellaneous) Energy Engineering and Power Technology Renewable Energy, Sustainability and the Environment Electrical and Electronic Engineering Control and Optimization Engineering (miscellaneous) http://dx.doi.org/10.3390/en13030532 <jats:p>As energy saving becomes more and more popular, electric load forecasting has played a more and more crucial role in power management systems in the last few years. Because of the real-time characteristic of electricity and the uncertainty change of an electric load, realizing the accuracy and stability of electric load forecasting is a challenging task. Many predecessors have obtained the expected forecasting results by various methods. Considering the stability of time series prediction, a novel combined electric load forecasting, which based on extreme learning machine (ELM), recurrent neural network (RNN), and support vector machines (SVMs), was proposed. The combined model first uses three neural networks to forecast the electric load data separately considering that the single model has inevitable disadvantages, the combined model applies the multi-objective particle swarm optimization algorithm (MOPSO) to optimize the parameters. In order to verify the capacity of the proposed combined model, 1-step, 2-step, and 3-step are used to forecast the electric load data of three Australian states, including New South Wales, Queensland, and Victoria. The experimental results intuitively indicate that for these three datasets, the combined model outperforms all three individual models used for comparison, which demonstrates its superior capability in terms of accuracy and stability.</jats:p> Multi-Objective Particle Swarm Optimization Algorithm for Multi-Step Electric Load Forecasting Energies
spellingShingle	Yang, Yi, Shang, Zhihao, Chen, Yao, Chen, Yanhua, Energies, Multi-Objective Particle Swarm Optimization Algorithm for Multi-Step Electric Load Forecasting, Energy (miscellaneous), Energy Engineering and Power Technology, Renewable Energy, Sustainability and the Environment, Electrical and Electronic Engineering, Control and Optimization, Engineering (miscellaneous)
title	Multi-Objective Particle Swarm Optimization Algorithm for Multi-Step Electric Load Forecasting
title_full	Multi-Objective Particle Swarm Optimization Algorithm for Multi-Step Electric Load Forecasting
title_fullStr	Multi-Objective Particle Swarm Optimization Algorithm for Multi-Step Electric Load Forecasting
title_full_unstemmed	Multi-Objective Particle Swarm Optimization Algorithm for Multi-Step Electric Load Forecasting
title_short	Multi-Objective Particle Swarm Optimization Algorithm for Multi-Step Electric Load Forecasting
title_sort	multi-objective particle swarm optimization algorithm for multi-step electric load forecasting
title_unstemmed	Multi-Objective Particle Swarm Optimization Algorithm for Multi-Step Electric Load Forecasting
topic	Energy (miscellaneous), Energy Engineering and Power Technology, Renewable Energy, Sustainability and the Environment, Electrical and Electronic Engineering, Control and Optimization, Engineering (miscellaneous)
url	http://dx.doi.org/10.3390/en13030532