一种改进果蝇算法优化神经网络短期负荷预测模型

在神经网络的训练过程中，由于随机初始化网络参数使得算法收敛速度慢、容易陷入局部极值，因此，本文提出了一种改进果蝇优化算法(IFOA)用于优化神经网络的初始连接权值和阈值，以实现全局优化。首先采用在BP神经网络中加入从输入到输出的连接的网络(BPNN-DIOC，直连BP神经网络)，减少隐含层所需的神经元个数，降低网络在训练过程中调整的参数个数和加快网络训练速度，以提高电力负荷的预测精度和网络的泛化能力。然后综合IFOA和BPNN-DIOC，构建了基于IFOA优化BPNN-DIOC的负荷预测模型。最后，为了验证本文模型的有效性，以AEMO中新南威尔士州2105年9月份的数据为例进行了仿真测试，IFOA-BPNN-DIOC模型的预测平均绝对误差百分比为0.6357%，均方根误差为0.0118，并将该结果与本文其它模型的预测结果进行比较。结果表明，本文模型是一种更加有效的短期负荷预测方法。

An Improved Fruit Fly Optimization Algorithm Algorithm to Optimize Neural Network Short Term Load Forecasting Model

In the training process of neural network, the convergence speed of the algorithm is slow and easy to fall into the local extreme due to the random initialization of the network parameters. Therefore, an improved fruit fly optimization algorithm (IFOA) is proposed to optimize the initial connection weights and thresholds of neural network for global optimization. Firstly, the BPNN-DIOC model, that is add the connections from the input to the output based on the BP neural network is used to decrease the number of neurons required by the hidden layer, reduce the number of parameters adjusted in the training process of network and speed up the network training, to improve the prediction accuracy of power load and generalization ability of network. Then, combining IFOA and BPNN-DIOC, a load forecasting model based on IFOA optimized BPNN-DIOC is constructed. Finally, in order to verify the validity of this model, a simulation test was conducted using the data of AEMO in New South Wales on September 2015 as example, the average absolute error percentage of IFOA-BPNN-DIOC model is 0.6357% and the root mean square error is 0.0118, and the result is compared with the prediction results of other models in this paper. The results show that this model is a more effective method for short-term load forecasting.