Analysis and forecast of supercomputing power load based on time series transformer

Since 2020, the national power situation has been very tense. With the national "carbon peak" and "carbon neutral" goals proposed, and the gradual establishment of a green, low-carbon and environmentally friendly economic system, if the future power load of the supercomputing center can be accurately predicted the situation, and according to the power dispatch and regulation, can achieve the purpose of energy saving and emission reduction. Power load forecasting using statistical and artificial intelligence technologies still has a lot of issues today. In order to increase the accuracy and stability of power load adjustment, this research suggests a novel Transformer approach based on the attention mechanism. It establishes a power load analysis and prediction model. This paper can provide the supercomputing center with the load demand situation in the future, and it has important application value for the regional power management department to formulate development plans and effectively manage the load, and is of great significance for responding to the national energy conservation and emission reduction policy.