结合受限玻尔兹曼机的递归神经网络电力系统短期负荷预测

Short-term power load forecasting using recurrent neural network with restricted Boltzmann machine

短期负荷预测的重要性随着电力企业的发展不断提高。传统的负荷预测虽然已经发展相对成熟,但现阶段对负荷预测的准确性要求逐渐提高。为满足发展需要,则要对现有的方法进行改进或建立新的预测方法。通过分析负荷预测数据周期性及周期内的特征,结合递归神经网络在分析时间序列数据的独特优势和受限玻尔兹曼机的强大的无监督学习能力,对结合受限玻尔兹曼机的递归神经网络的工作原理及训练过程进行了阐述。利用该网络进行了电力负荷数据预测实验验证并与其他神经网络进行了比较性实验。结果表明,所提出的神经网络较其他网络在电力短期负荷预测实验中有更高的准确性。

The importance of short-term power load forecasting continues to improve with the development of power enterprises.Although the traditional load forecast has been developed relatively mature,the load forecast accuracy requirements gradually increase now.To meet the development needs,existing methods should be improved or new methods of prediction should be established.This paper analyzes the periodic load forecasting and periodic characteristics of load forecasting data.It also combines recursive neural networks with Limited Boltzmann machine's strong unsupervised learning ability in analyzing the unique advantages of time series data.The network principle and training process of the combination are described and the electric load data are predicted by experiment.The experiment compares the accuracy of the network and other networks in short-term load forecasting.The results show that the neural network proposed in this paper is more accurate than other networks in the power short-term load prediction experiment.