Zhao Mingzhi, Guo Guangrong, Fan Lijun, Han Long, Yu Qiancheng, Wang Ziyi
The College of Computer Science and Engineering, North Minzu University, Yinchuan, 750021, China.
Ningxia Hanas Gas Group Co., Ltd, Yinchuan, 750021, China.
Sci Rep. 2024 Sep 2;14(1):20343. doi: 10.1038/s41598-024-70384-9.
Due to changes in urban residents' consumption habits and lifestyles, accurately predicting natural gas consumption has become increasingly important. To address this issue, this paper proposes a forecasting model that combines Ensemble Learning (EL), Variational Mode Decomposition (VMD), Transformer, and LSTM. First, XGBoost, CatBoost, and LightGBM are used as base learners in the ensemble learning framework, with the predictions generated by the ensemble model integrated into the original dataset. Next, the VMD method is employed to decompose the natural gas load sequence into several intrinsic mode functions (IMFs), effectively extracting the inherent features of the natural gas load sequence. Finally, the data is input into the Transformer-ResLSTM network for prediction. This network replaces the original Transformer decoder structure with an LSTM network and fully connected layers, creating a new decoder structure. Additionally, a residual connection mechanism is introduced in both the encoder of the Transformer network and the new decoder structure. Experimental results show that, compared to traditional models such as ARIMA, Transformer, GRU, and LSTM, the proposed hybrid model significantly improves prediction accuracy, reducing MSE by 92-98% and MAE by 74-83%. In summary, this method demonstrates significant potential and practical value in enhancing the accuracy of natural gas load forecasting.
由于城市居民消费习惯和生活方式的变化，准确预测天然气消费量变得越来越重要。为解决这一问题，本文提出了一种结合集成学习（EL）、变分模态分解（VMD）、Transformer和长短期记忆网络（LSTM）的预测模型。首先，将极端梯度提升（XGBoost）、类别提升（CatBoost）和轻量级梯度提升机（LightGBM）用作集成学习框架中的基础学习器，将集成模型生成的预测结果整合到原始数据集中。接下来，采用VMD方法将天然气负荷序列分解为若干个本征模态函数（IMF），有效提取天然气负荷序列的固有特征。最后，将数据输入Transformer-ResLSTM网络进行预测。该网络用LSTM网络和全连接层取代了原来的Transformer解码器结构，创建了一种新的解码器结构。此外，在Transformer网络的编码器和新的解码器结构中都引入了残差连接机制。实验结果表明，与自回归积分滑动平均模型（ARIMA）、Transformer、门控循环单元（GRU）和LSTM等传统模型相比，所提出的混合模型显著提高了预测精度，均方误差（MSE）降低了92%-98%，平均绝对误差（MAE）降低了74%-83%。综上所述，该方法在提高天然气负荷预测精度方面具有显著的潜力和实用价值。
