Multiobjective Optimal Dispatching of Smart Grid Based on PSO and SVM

Man Bao, Hongqi Zhang, Hao Wu, Chao Zhang, Zixu Wang, Xiaohui Zhang
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引用次数: 2

Abstract

The optimization of microgrid is an important part of smart grid. The global energy consumption is seriously greater than the energy it has, and the environmental pollution brought by it should not be underestimated. If we want to reduce their impact, introducing the optimization of microgrid is a good solution. Short-term load forecasting is a very important prerequisite for microgrid optimization, which lays a solid foundation for the realization of the development goal of environmental protection and the improvement of the economic benefits of microgrid. In this paper, a Multi-PSO-SVM forecasting model is proposed to forecast the actual load. By simulating four prediction models with three different samples, we can see that the average predicted value and actual load value of Multi-PSO-SVM algorithm in the three different samples are almost less than 10 MV. Compared with the other three algorithms, Multi-PSO-SVM is superior in accurately predicting the load value at each time point, which provides important conditions for the success of microgrid optimization.
基于粒子群算法和支持向量机的智能电网多目标优化调度
微电网优化是智能电网的重要组成部分。全球能源消耗严重大于其拥有的能源,由此带来的环境污染不容小觑。如果我们想要减少它们的影响,引入微电网的优化是一个很好的解决方案。短期负荷预测是微网优化的一个非常重要的前提,为实现环境保护的发展目标和提高微网的经济效益奠定了坚实的基础。本文提出了一种多粒子群-支持向量机预测模型来预测实际负荷。通过对3个不同样本的4种预测模型进行仿真,我们可以看到,Multi-PSO-SVM算法在3个不同样本下的平均预测值和实际负载值几乎都小于10 MV。与其他三种算法相比,Multi-PSO-SVM在准确预测各时间点负荷值方面具有优势,这为微网优化的成功提供了重要条件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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