Generalized load graphical forecasting method based on modal decomposition  

作　　者：Lizhen Wu Peixin Chang Wei Chen Tingting Pei 

机构地区：[1]College of Electrical and Information Engineering,Lanzhou University of Technology,Lanzhou 730050,P.R.China [2]School of Electrical Data Engineering,University of Technology Sydney,NSW,2007,Australia

出　　处：《Global Energy Interconnection》2024年第2期166-178,共13页全球能源互联网（英文版）

基　　金：supported by the National Natural Science Foundation of China(Grant No.62063016).

摘　　要：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 for generalized loads based on modal decomposition.First,the datasets are normalized and feature-filtered by comparing the results of Xtreme gradient boosting,gradient boosted decision tree,and random forest algorithms.Subsequently,the generalized load data are decomposed into three sets of modalities by modal decomposition,and red,green,and blue(RGB)images are generated using them as the pixel values of the R,G,and B channels.The generated images are diversified,and an optimized DenseNet neural network was used for training and prediction.Finally,the base load,wind power,and photovoltaic power generation data are selected,and the characteristic curves of the generalized load scenarios under different permeabilities of wind power and photovoltaic power generation are obtained using the density-based spatial clustering of applications with noise algorithm.Based on the proposed graphical forecasting method,the feasibility of the generalized load graphical forecasting method is verified by comparing it with the traditional time-series forecasting method.

关 键 词：Load forecasting Generalized load Image processing DenseNet Modal decomposition 

分 类 号：TM715[电气工程—电力系统及自动化]