The invention discloses a sine normalization method for a power forecast model of a wind power plant, and belongs to the field of wind power forecast. The method comprises the following steps of: 1), obtaining n groups of numerical weather forecast data and output power data of the wind power plant; 2), initializing a BP (Back Propagation) neural network; 3), carrying out linear normalization processing on wind speed, wind direction sine, wind direction cosine, temperature, atmospheric pressure and humidity respectively, carrying out sine normalization processing on the output power data of the wind power plant; and 4), forecasting by using x'new as an input value of the BP neural network, and carrying out backward normalization on the obtained forecast result. The sine normalization method for the power forecast model of the wind power plant, disclosed by the invention, has the beneficial effects that the strong universality is realized; the forecast precision of a neural network power forecast model is improved remarkably; and the method is simple and feasible, and can be implemented without varying out modification on the original neural network power forecast model.