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

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 stabi...

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Vydáno v:Energies (Basel) Ročník 13; číslo 3; s. 532
Hlavní autoři: Yang, Yi, Shang, Zhihao, Chen, Yao, Chen, Yanhua
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 2020
Témata:
Accuracy
electric load forecasting
Electricity
extreme learning machine
Machine learning
Mathematical models
multi-objective particle swarm optimization algorithm
Neural networks
Optimization algorithms
recurrent neural network
Short term
Support vector machines
Time series
Wavelet transforms
ISSN:1996-1073, 1996-1073
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Shrnutí: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.
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ISSN:1996-1073
1996-1073
DOI:10.3390/en13030532