Abstract: Based on daily maximum electric loads and meteorological data in the summer (June-August) from 2006 to 2017 in Beijing, the relationship between electric load and meteorological factors is diagnosed. Using the BP (Back Propagation) neural network algorithm, two maximum electric power load prediction models are established and evaluated. The results indicate that (1) the basic electric load on weekends in Beijing in the summer is much less than that in working days, which should be distinguished when being removed; (2) the influence of meteorological factors on meteorological load has cumulative effect, and the correlation between them is the highest for two days of accumulation; (3) taking the actual situation into account, two different daily maximum electric load forecasting models are established based on different independent variables. Comparing the prediction results with actual data, both of the forecasting models show good prediction performance that can meet the actual demand of the power sector. The forecasting model with meteorological load of the previous day as an independent variable shows better prediction effect.