The invention discloses a method for forecasting short-term wind-electricity power. The method comprises the following steps: (1) acquiring data of global large-scale weather forecast fields; (2) establishing an RAMS model which calculates forecast fields such as wind speed, temperature, air pressure, and the like by using the following equation set; (3) inputting the data of the large-scale weather forecast fields acquired in step (1) into the RAMS model established in step (2) to acquire wind direction-wind speed forecast data within the range of a wind-electricity field; (4) establishing a wind-electricity power statistical model by using a BP neural network and combining historical forecast data of the wind speed, wind direction and the air pressure within the range of the wind-electricity field and power recording data of the wind-electricity field in the same period; and (5) taking the wind direction-wind speed data of a forecast date as original parameters and inputting the data into the wind-electricity power statistical model to calculate the wind-electricity power forecast result of a forecast day. Due to adoption of 90-meter resolution topographic data, the method can calculate regions of flat terrain, hilly terrain, complex terrain, and the like by using different resolutions, and has the advantages of flexible forecast scheme and less limiting condition.