基于模态分解的广义负荷图像化预测方法OACSTPCDEI
Generalized load graphical forecasting method based on modal decomposition
在“双碳”背景下,电力负荷受多重因素耦合影响,从传统的“纯负荷”逐渐演变为具有“负荷”+“电源”双重特性的广义负荷.广义负荷由于其复杂性和不确定性,传统的时间序列预测方法将不再适用.本文从图像处理的角度出发,提出了一种基于模态分解的广义负荷图像化短期预测方法.首先,通过XGBooste;GBDT;RF算法的结果对比,将数据集进行归一化处理和特征筛选.然后利用模态分解将广义负荷数据分解为3组模态,将其作为R;G;B三通道的像素值生成三基色（RGB）…查看全部>>
In a"low-carbon"context,the power load is affected by the coupling of multiple factors,which gradually evolves from the traditional"pure load"to the generalized load with the dual characteristics of"load + power supply."Traditional time-series forecasting methods are no longer suitable owing to the complexity and uncertainty associated with generalized loads.From the perspective of image processing,this study proposes a graphical short-term prediction method…查看全部>>
吴丽珍;常培鑫;陈伟;裴婷婷
负荷预测广义负荷图像化处理DenseNet模态分解
Load forecastingGeneralized loadImage processingDenseNetModal decomposition
《全球能源互联网（英文）》 2024 (2)
166-178,13
This study was supported by the National Natural Science Foundation of China(Grant No.62063016).
评论