数学优化
计算机科学
混乱的
水准点(测量)
局部最优
差异进化
趋同(经济学)
人口
算法
局部搜索(优化)
人工智能
数学
社会学
地理
经济
人口学
经济增长
大地测量学
作者
Guijuan Wang,Xinheng Wang,Zuoxun Wang,Chunrui Ma,Zengxu Song
出处
期刊:Mathematics
[MDPI AG]
日期:2021-12-22
卷期号:10 (1): 28-28
被引量:19
摘要
Accurate power load forecasting has an important impact on power systems. In order to improve the load forecasting accuracy, a new load forecasting model, VMD–CISSA–LSSVM, is proposed. The model combines the variational modal decomposition (VMD) data preprocessing method, the sparrow search algorithm (SSA) and the least squares support vector machine (LSSVM) model. A multi-strategy improved chaotic sparrow search algorithm (CISSA) is proposed to address the shortcomings of the SSA algorithm, which is prone to local optima and a slow convergence. The initial population is generated using an improved tent chaotic mapping to enhance the quality of the initial individuals and population diversity. Second, a random following strategy is used to optimize the position update process of the followers in the sparrow search algorithm, balancing the local exploitation performance and global search capability of the algorithm. Finally, the Levy flight strategy is used to expand the search range and local search capability. The results of the benchmark test function show that the CISSA algorithm has a better search accuracy and convergence performance. The volatility of the original load sequence is reduced by using VMD. The optimal parameters of the LSSVM are optimized by the CISSA. The simulation test results demonstrate that the VMD–CISSA–LSSVM model has the highest prediction accuracy and stabler prediction results.
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