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.