RESEARCH PAPER
Forecasting short-term electric load using extreme learning machine with improved tree seed algorithm based on Lévy flight
Orest Kochan
1,5
1
Hubei University of Technology, School of Computer Science, Wuhan, Hubei, 430068, PR China
2
Lublin University of Technology, Department of Automation, ul. Nadbystrzycka 36, 20-618 Lublin, Poland
3
Wuhan Fiberhome Technical Services Co., Ltd., Wuhan, Hubei, 430074, PR China
4
Wuhan FiberHome Telecommunication Technologies Co., Ltd., Wuhan, Hubei, 430074, PR China
5
Lviv Polytechnic National University, Bandery 12, 79013 Lviv, Ukraine
Publication date: 2022-03-31
Eksploatacja i Niezawodność – Maintenance and Reliability 2022;24(1):153-162
HIGHLIGHTS
- Forecasting Short-term Electric Load using Extreme Learning Machine is considered.
- Improved Tree Seed Algorithm based on Lévy flight is proposed.
- This method has better convergence, prediction accuracy and stability than similar ones.
KEYWORDS
short-term electric load forecastextreme learning machineLévy flighttree-seed algorithmKernel principal component analysis
ABSTRACT
In recent years, forecasting has received increasing attention since it provides an important basis for the effective operation of power systems. In this paper, a hybrid method, composed of kernel principal component analysis (KPCA), tree seed algorithm based on Lévy flight (LTSA) and extreme learning machine (ELM), is proposed for short-term load forecasting. Specifically, the randomly generated weights and biases of ELM have a significant impact on the stability of prediction results. Therefore, in order to solve this problem, LTSA is utilized to obtain the optimal parameters before the prediction process is executed by ELM, which is called LTSA-ELM. Meanwhile, the input data is extracted by KPCA considering the sparseness of the electric load data and used as the input of LTSA-ELM model. The proposed method is tested on the data from European network on intelligent technologies (EUNITE) and experimental results demonstrate the superiority of the proposed approaches compared to the other methods involved in the paper.
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| eISSN: | 2956-3860 |
| ISSN: | 1507-2711 |
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