The application provides a multi-element two-stage adaptive accumulation prediction method for a regional
integrated energy system; firstly, a new preprocessing module based on
ensemble learning is provided to preprocess original input data, provide a reliable data basis for TAPM, and determine key input variables of multi-
energy load prediction; then, the training results of four predictors are adaptively integrated in the first stage of TAPM to enhance the generalization ability of the model, wherein a new collaborative atomic
chaotic search algorithm is provided to
train the predictor hyperparameters and intermediate
data set of TAPM, so that the adaptive accumulation of the predictor in TAPM is realized; finally,
peak load prediction correction in the
power consumption peak period is carried out in the second stage of TAPM; the energy
cascade utilization efficiency is improved, the regional
integrated energy system is widely concerned, the multi-
energy load prediction is very crucial for the planning of the regional
integrated energy system, and effective decisions can be made by managers for the carbon peak target.