Model training method, power prediction method, and device

A training device, comprising a processor and a memory, wherein the processor is coupled to the memory;

• the memory is configured to store a program;
• and the program, when executed by the processor, causes the device to:
• obtain a dataset, wherein the dataset comprises historical power data and historical meteorological data in a preset duration;
• determine a first weight of a first sub-loss function and a second weight of a second sub-loss function based on the dataset, wherein the first sub-loss function is a loss function corresponding to the first model, the second sub-loss function is a loss function corresponding to the second model, the first model and the second model are used to represent input data, and data represented by the first model is not completely the same as data represented by the second model;
• determine a target loss function based on the first sub-loss function, the second sub-loss function, the first weight, and the second weight;
• and
• train the model based on the dataset and the target loss function, to obtain a trained model, wherein the trained model comprises at least a trained first model and a trained second model.

An execution device, comprising a processor and a memory, wherein the processor is coupled to the memory;

• the memory is configured to store a program;
• and the program, when executed by the processor, causes the device to:
• obtain input data, wherein the input data comprises historical power data in a first preset duration and predicted meteorological data in a second preset duration;
• and
• input the input data to a trained model, to obtain predicted power data at at least one preset moment in the second preset duration, wherein the trained model comprises at least a trained first model and a trained second model, the trained first model and the trained second model are used to represent the input data, data represented by the model is not completely the same as data represented by the second model, and representation output by the trained first model and representation output by the trained second model are used to obtain the predicted power data at the at least one preset moment in the second preset duration.