基于预训练GRU-LightGBM的电力负荷预测

本文研究工作围绕电网中的核心环节电负荷预测展开.在总结分析之前研究学者的研究成果基础上,提出了基于预训练GRU与LightGBM相结合的方法.该方法首先使用电负荷数据训练一个特征提取网络GRU,然后使用该网络进行时序特征的提取,并将提取到的时序特征与非时序特征使用LightGBM进行电负荷的预测.本方法的创新点在于提出了预训练网络来扩充特征,充分融合时序特征及非时序特征.并且考虑到电网的地区差异性,在整体训练过程中将GRU网络参数进行了适应性微调.保证提取到的时序特征是符合当前地域特点的.通过仿真实验最终发现该方法在各项指标上取得了2％的提升.

Electric Load Forecasting Based on Pre-Training GRU-LightGBM

This study focuses on the electric load forecasting of the core link in the power grid. On the basis of summarizing and analyzing the research results of previous researchers, a method based on the combination of pre-training GRU and LightGBM is proposed. This method first uses electrical load data to train a feature extraction network GRU, then uses the network to extract timing features, and uses LightGBM to predict the electrical load of the extracted timing features and non-sequential features. The innovation of this method is to propose a pre-training network to expand the features and fully integrate the timing features and non-timing features. And taking into account the regional differences of the power grid, the GRU network parameters were adaptively fine-tuned during the overall training process. Ensure that the extracted time series features are consistent with the current regional characteristics. Finally, it is found through simulation experiments that this method has achieved a 2% improvement in various indicators.