期刊
SUSTAINABILITY
卷 9, 期 7, 页码 -出版社
MDPI
DOI: 10.3390/su9071188
关键词
load forecasting; least square support vector machine; sperm whale algorithm; feature selection
资金
- National Natural Science Foundation of China (NSFC) [71501071]
- China Postdoctoral Science Foundation [2014M550937]
- Ministry of Education in China [14JF005]
- Beijing Social Science Fund [16YJC064]
- Fundamental Research Funds for the Central Universities [2017MS059]
Short-term power load forecasting is an important basis for the operation of integrated energy system, and the accuracy of load forecasting directly affects the economy of system operation. To improve the forecasting accuracy, this paper proposes a load forecasting system based on wavelet least square support vector machine and sperm whale algorithm. Firstly, the methods of discrete wavelet transform and inconsistency rate model (DWT-IR) are used to select the optimal features, which aims to reduce the redundancy of input vectors. Secondly, the kernel function of least square support vector machine LSSVM is replaced by wavelet kernel function for improving the nonlinear mapping ability of LSSVM. Lastly, the parameters of W-LSSVM are optimized by sperm whale algorithm, and the short-term load forecasting method of W-LSSVM-SWA is established. Additionally, the example verification results show that the proposed model outperforms other alternative methods and has a strong effectiveness and feasibility in short-term power load forecasting.
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