基于混合神经网络的短期电力负荷预测方法

Short-term load forecasting method based on hybrid neural network

电力负荷预测对电网的经济运行至关重要,为提高短期负荷预测精度并降低混合神经网络模型的训练时间,提出了一种基于多层感知器(MLP)的基础子网、简单循环单元(SRU)与主成分分析(PCA)的短期电力负荷预测模型。首先,考虑影响电力负荷变化的各种因素,建立负荷预测输入特征集;其次,利用PCA对输入网络的部分特征进行变换并降维;最后,将经过PCA处理后得到的全新数据信息作为模型的输入,并结合Adam梯度下降算法进行训练,输出负荷预测的结果。通过仿真实验结果表明,包含SRU的混合模型在全部测试集样本上的MAPE为2.126%,远低于仅有子网的单一模型与包含DNN的混合模型,而与包含LSTM的混合模型相比,训练时间却降低了22.74%,同时PCA的应用也使得模型的收敛速度加快,极大地减小了训练轮数。

Load forecasting is crucial to the economic operation of the power grid. In order to improve the accuracy of short-term load forecasting and reduce the training time of the hybrid neural network, a short-term load forecasting method based on basic network with multilayer perceptron(MLP), simple recurrent units(SRU) and principal component analysis(PCA) is proposed. Firstly, the method considers various power load influencing factors to establish input feature sets of the load forecasting task, and uses PCA to transform and reduce the historical load and temperature characteristics which are the original inputs of the network. Then, the method uses new data information obtained after PCA as the inputs of the hybrid neural network model, and trains the network with Adam gradient descent algorithm. Finally, the outputs of the proposed model are load forecasting results. The results of the experiments show that the MAPE of the hybrid model including SRU on all test set samples is 2.126%, which is much lower than that of the single model with only basicnet and the hybrid model including DNN, and compared with the hybrid model including LSTM, the training time is reduced by 22.74%, and the application of PCA also accelerates the convergence of the model, which greatly reduces the number of training epochs.