基于交变粒子群BP网络的电力系统短期负荷预测

发布时间：2018-06-04 16:24

本文选题：粒子群算法 + BP神经网络　；参考：《计算机科学》2017年S2期

【摘要】：短期负荷预测是电力系统正常运行的关键环节,合理的发电计划依靠准确的负荷预测,因此提出交变粒子群算法来优化BP网络模型以预测电力短期负荷。针对依靠先前的经验来确定BP神经网络的权值缺少理论依据的问题,采用交变粒子算法优化BP神经网络权值,以减少通过神经网络预测模型求解电力短期负荷预测带来的误差。实验证明,经过优化的BP神经网络预测模型比传统的BP神经网络预测模型的误差更小,更加接近实际电力负荷。
[Abstract]:The short-term load forecasting is the key link in the normal operation of the power system. The reasonable generation plan relies on the accurate load forecasting. Therefore, the alternating particle swarm optimization (PSO) algorithm is proposed to optimize the BP network model to predict the short-term load of the electric power system. The problem of the lack of theoretical basis for determining the weight value of the BP neural network is determined by the previous experience, and the alternating particle is adopted. The algorithm optimizes the weights of the BP neural network to reduce the error caused by the prediction model of the neural network. The experiment shows that the optimized BP neural network prediction model is smaller than the traditional BP neural network prediction model and is closer to the actual electrical load.
【作者单位】：广西大学电气工程学院;广西职业技术学院机械与汽车技术系;
【分类号】：TM715;TP18

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