The accurate short-term metro station load prediction can help the metro operation department to make the power consumption purchase decisions, which will further ensure the stability and cost-efficiency of the metro station’s power supply. Considering the non-linearity and non-stationarity characteristics of metro station load series, this paper proposes a short-term metro station power lighting load prediction method based on TimesNet. Firstly, data are collected on factors that may affect power lighting load in metro stations. Secondly, the Pearson correlation coefficient method is used, and the main correlated characteristics of the power lighting load are selected from the multi-dimensional features. Thirdly, TimesNet is trained to predict the short-term power lighting load. Finally, a simulation experiment is conducted to compare the proposed methods with six other typical load prediction methods. The experimental results demonstrate that TimesNet can reduce the MAPE to 3.8% and is generally superior to several other prediction methods.

multi-dimensional features,power lighting load,short-term prediction,TimesNet