Current electrical load forecasting models are constrained by high data complexity

data scarcity

limited generalization

and in?sufficient adaptability to dynamic socio-economic factors

impeding their utility in sophisticated grid planning. To meet the planning andscheduling requirements of power grids or large-scale wind

solar

thermal

storage

grid

and load energy base projects

an integrated tech?nology has been proposed. Grey forecasting

spatial load density forecasting

variational autoencoders

and deep causal convolutional neu?ral networks are combined for medium to long-term load forecasting. The introduction of an ordered weighted averaging differential opera?tor amalgamates various predictive techniques

thereby refining accuracy. The experimental results demonstrate that the proposed methodexhibits higher accuracy and robustness compared to traditional methods

particularly in the context of long-term electric load forecasting

effectively enhancing the reliability and applicability of the predictions. This technology effectively overcomes issues of data complexity

data scarcity and model generalization inherent in conventional methods

while adjusting to socio-economic dynamics. It provides substan?tial decision-making support for the planning and evolution of power networks and large-scale integrated energy projects.