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

Yi Yang, Zhihao Shang, Yao Chen and Yanhua Chen
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Yi Yang: School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
Zhihao Shang: Department of Mathematics and Computer Science, Free University of Berlin, 14195 Berlin, Germany
Yao Chen: School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China
Yanhua Chen: School of Information Engineering, Zhengzhou University, Zhengzhou 450000, China

Energies, 2020, vol. 13, issue 3, 1-19

Abstract: 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.

Keywords: electric load forecasting; extreme learning machine; recurrent neural network; support vector machines; multi-objective particle swarm optimization algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (10)

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