ABSTRACT The total electricity consumption (TEC) can accurately reflect the operation of the national economy, and the forecasting of the TEC can help predict the economic development trend, as well as provide insights for the formulation of macro policies. Nowadays, high-frequency and massive multi-source data provide a new way to predict the TEC. In this paper, a “seasonal-cumulative temperature index” is constructed based on high-frequency temperature data, and a mixed-frequency prediction model based on multi-source big data (Mixed Data Sampling with Monthly Temperature and Daily Temperature index, MIDAS-MT-DT) is proposed. Experimental results show that the MIDAS-MT-DT model achieves higher prediction accuracy, and the “seasonal-cumulative temperature index” can improve prediction accuracy.