To give full play to the value of wind power data and bring extra profit to a wind farm, this paper proposes a wind power data pricing method based on alliance cooperation game theory. First, a wind power data trading process and pricing approach considering third-party supervision are proposed. Secondly, adjacent station data is obtained through data trading, and a vector autoregressive model is used to improve wind power forecasting accuracy, thereby reducing imbalance costs in electricity market transactions, increasing the overall generation profit of wind power plants, and reflecting the value of the traded data. Then, the excess profit generated before and after data trading (i.e., the increase in generation profit using traded data) is distributed to the data sellers as the pricing of the traded data using an alliance cooperation game, quantitatively measuring the value of the data. Finally, considering the spatio-temporal characteristics of wind power, the data trading parties are determined by maximizing the data value, achieving fair and reasonable data pricing. The case analysis results demonstrate that the proposed method can enhance wind power forecasting accuracy, increase wind farm generation profits, and achieve fairness and rationality in wind power data pricing.