基于IABC-Elman神经网络的电厂耗煤量短期预测

针对电厂耗煤量具有不确定性的特点及传统Elman神经网络利用梯度下降训练网络参数易陷于局部最优的缺点,基于人工蜂群(ABC)算法,提出了一种改进蜜源更新方式和跟随蜂选择引领蜂方式的改进ABC优化算法,结合进煤量、存煤量和发电量,建立了Elman神经网络电厂耗煤量短期预测模型(IABC-Elman)。实际算例表明,基于IABC-Elman电厂耗煤量短期预测模型结果能达到耗煤量短期预测的标准,与传统神经网络相比具有更高的预测精度。

Short-term Prediction of Coal Consumption of Power Plants Based on IABCElman Neural Network

Due to the uncertainty of coal consumption in power plants and the shortcomings of traditional Elman neural network using gradient descent training network parameters to be trapped in local optimum,an improved artificial bee colony (ABC) is proposed to update the honey source and follow the bee.The optimization algorithm of artificial bee colony that leads the new method of bee is selected,and the short-term prediction model of coal consumption of Elman neural network power plant (IABC-Elman) is established by combining coal intake,coal storage and power generation.The actual example shows that the short-term prediction model of power plant based on IABC-Elman neural network can achieve the short-term prediction of coal consumption,and has higher prediction accuracy than traditional neural network.