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.