自编码器与PSOA-CNN结合的短期负荷预测模型

提出了一种自编码器与PSO算法优化卷积神经网络结合的电力系统短期负荷预测模型。首先利用自编码器对相关变量数据进行处理,降低所需数据的噪声变量,提高预测效率;然后利用粒子群算法对卷积神经网络的权值和阈值进行优化,可有效提高预测模型的预测精度和预测速度。通过对实际电网的负荷数据进行仿真,验证了模型具有较高的预测精度。

Short-term load forecasting model based on autoencoder and PSOA-CNN

A short-term load forecasting model which combines the autoencoder and convolutional neural network optimized by particle swarm optimization is proposed. Firstly, the autoencoder is used to process the relevant variable data, reduce the noise variable of the required data, and improve the prediction efficiency. Then particle swarm optimization is used to optimize the weight and threshold of the convolutional neural network., which can effectively improve the prediction accuracy and prediction speed of the prediction model. By simulating the load data of the actual power grid, it is verified that the proposed model has higher prediction accuracy.