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首页> 外文期刊>International Journal of Applied Mathematics & Statistics >Short-term Load Forecasting based on Support Vector Machine Optimized by Catfish Particle Swarm Optimization Algorithm
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Short-term Load Forecasting based on Support Vector Machine Optimized by Catfish Particle Swarm Optimization Algorithm

机译：基于鲶鱼粒子群优化算法优化支持向量机的短期负荷预测

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摘要

In order to accurately, effectively forecast short-term load, a short-term load forecasting based on support vector machine optimized by catfish particle swarm optimization algorithm (CFPSO-SVM) is proposed. First, the short-term load time series is reconstructed based on chaos theory, and then the support vector machine (SVM) parameters are taken as a particle location string, and catfish effect is introduced to overcome the shortcomings of particle swarm algorithm to find the optimal parameters of support vector machine through the particle interactions. Finally, short-term load forecasting model is built according to the optimum parameters. The model performance is test by simulation experiment and the results show that, compared with other forecasting models, CFPSO-SVM accelerates the parameters optimizing speed of support vector machine and improves the forecasting precision of short term load, and it is more suitable for short-term load forecasting needs.
机译：为了准确地，有效地预测短期负荷，提出了基于鲶鱼粒子群优化算法（CFPSO-SVM）优化的支持向量机的短期负荷预测。 首先，基于混沌理论重建短期负载时间序列，然后将支持向量机（SVM）参数作为粒子定位串，并引入鲶鱼效应以克服粒子群算法的缺点来找到 通过粒子相互作用的支持向量机的最佳参数。 最后，根据最佳参数建立短期负荷预测模型。 模型性能是通过仿真实验进行测试，结果表明，与其他预测模型相比，CFPSO-SVM加速了支持向量机的速度的参数，提高了短期负载的预测精度，更适合短 - 术语负载预测需求。

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