An intelligent hybrid short-term load forecasting model optimized by switching delayed PSO of micro-grids

被引:12
作者
Deng, Buqing [1 ]
Peng, Daogang [1 ]
Zhang, Hao [2 ]
Qian, Yuliang [1 ]
机构
[1] Shanghai Univ Elect Power, Sch Automat Engn, Shanghai 200090, Peoples R China
[2] Tongji Univ, Sch Elect & Informat, Shanghai 200092, Peoples R China
关键词
EXTREME LEARNING-MACHINE; SUPPORT VECTOR REGRESSION; WAVELET TRANSFORM; NEURAL-NETWORK; KALMAN FILTER;
D O I
10.1063/1.5021728
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Short-term load forecasting is an important part in the energy management of micro-grids, and the forecasting error directly affects the economic efficiency of operation. Considering the strong randomness and high volatility of loads on the user side of micro-grids, a short-term load forecasting method based on Empirical Mode Decomposition (EMD), Extreme Learning Machine with different Kernels, and Switching Delayed Particle Swarm Optimization (SDPSO) is proposed. First, the history load dataset is decomposed into several independent Intrinsic Mode Functions (IMFs) by EMD, and the sample entropy values of the IMFs are calculated. According to the approximation degree of sample entropy values, the IMFs are divided into three categories. Then, ELM with different kernels is adopted to forecast the three categories. Finally, the prediction results are summed to obtain the final prediction result. SDPSO is used to optimize the relevant parameters in the forecasting model. Three micro-grids with different capacities are used to verify the model, and the experimental results demonstrate that the proposed forecasting model can provide better accuracy than the other two methods. The proposed model can provide practical reference for the efficient operation of micro-grids. Published by AIP Publishing.
引用
收藏
页数:16
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