基于EL-VMD-Transformer-ResLSTM的短期天然气负载预测
Mingzhi Zhao1, Guangrong Guo2, Lijun Fan2
1The College of Computer Science and Engineering, North Minzu University, Yinchuan, 750021, China.
Scientific reports
|September 2, 2024
概括
准确的天然气消耗预测至关重要. 本研究引入了一种混合模型,该模型结合了集体学习 (EL),变化模式分解 (VMD),变压器和LSTM,显著提高了预测准确性.
科学领域:
- 能源系统分析 能源系统分析
- 计算智能是一种计算智能.
- 时间序列预测时间序列预测
背景情况:
- 城市天然气消费受到不断变化的居民习惯和生活方式的影响.
- 准确预测天然气需求对于高效的能源管理和电网稳定性至关重要.
- 现有的预测模型往往难以捕捉天然气消耗数据中的复杂模式.
研究的目的:
- 为天然气消耗开发一种新的混合预测模型.
- 通过整合先进的机器学习技术来提高预测准确性.
- 在动态的城市环境中解决传统预测方法的局限性.
主要方法:
- 集体学习 (EL) 使用XGBoost,CatBoost和LightGBM作为基础学习者.
- 变化模式分解 (VMD) 用于将天然气负载序列分解为内在模式函数 (IMF).
- 一个新的变压器-ResLSTM网络,具有修改的解码器结构和剩余连接用于预测.
主要成果:
- 与ARIMA,变压器,GRU和LSTM相比,拟的混合动力模型表现出优异的性能.
- 平均平方误差 (MSE) 显著减少92-98%,平均绝对误差 (MAE) 显著减少74-83%.
- 该模型有效地提取了固有的特征,并提高了天然气负载预测的准确性.
结论:
- 混合式EL-VMD-变压器-ResLSTM模型在天然气消耗预测方面取得了重大进展.
- 该方法在能源管理和城市规划方面具有实践应用的巨大潜力.
- 准确的预测支持优化资源分配和电网可靠性.
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