基于交变粒子群BP网络的电力系统短期负荷预测

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。

Short-term Load Forecasting of Power System Based on Alternating Particle Swarm BP Network

Short-term load forecasting is a key link in the normal operation of power system.Based on accurate load forecasting,an alternating particle swarm optimization algorithm was proposed to optimize the BP network model to predict short-term load in this paper.In order to determine the weight of the BP neural network,the weights of the BP neural network were optimized by using the alternating particle algorithm to reduce the error caused by the neural network prediction model to solve the short term load forecasting.The experimental results show that the optimized BP neural network prediction model is less accurate than the traditional BP neural network prediction model and closer to the actual power load.