基于门控递归神经网络的电网日峰值负荷预测

Daily Peak Load Forecasting Based on Gating Recurrent Neural Network

日峰值负荷作为非线性、非平稳且波动的时间序列,难以准确预测。提出了一种结合动态时间规整(DTW)的门控递归神经网络(GRNN)用于准确预测日峰值负荷。利用DTW距离用于匹配最相似的负荷曲线,可以捕捉负荷变化趋势。采用热编码方案对离散变量进行编码,扩展其特征从而表征对负荷曲线的影响。提出了一种基于DTW的门控递归单元(DTW-GRU)算法用于日峰值负荷预测,并在欧洲智能技术网络(EUNITE)数据集上进行了测试。仿真结果表明,与其他算法相比,该算法的MAPE仅为1.01%。

As a non-linear,non-stationary and fluctuating time series,daily peak load is difficult to predict accurately.A gated recurrent neural network(GRNN)combined with dynamic time warping(DTW)is proposed to predict daily peak load accurately.DTWdistance is used to match the most similar load curve,which can capture the trend of load change.The thermal coding scheme is used to encode the discrete variables and extend their characteristics to represent the influence on the load curve.A DTW-GRU algorithm based on DTWis proposed for daily peak load forecasting,and it is tested on the European Intelligent Technology Network(EUNITE)dataset.Simulation results show that the MAPE of this algorithm is only 1.01%compared with other algorithms.