基于小样本集的网侧风电功率预测方法

风电功率预测多采用统计预测模型，为了达到可接受的预测精度，需要大量的历史数据对模型进行训练，不适用于缺少历史数据的新建风电场，为此提出基于小样本集的网侧风电功率预测方法。基于风电场少量的历史数据，运用支持向量机方法建立了网侧风电功率预测通用模型，并用此通用模型对风电场功率进行初步预测；在通用模型预测的基础上，利用区域内风电场的特征参数对这一网侧通用模型进行辨识和修正，从而得到区域电网网侧风电功率预测结果。实际算例验证了基于小样本集的预测方法的可行性，实际预测精度较好，说明该方法适于历史数据样本较小的风电场的功率预测，能够减少功率预测中统计预测方法对数据的依赖。

Wind Power Forecasting for Power Grid Based on Small Sample Set

At present, the widely used statistical prediction model for wind power needs a lot of the historical data for training to achieve acceptable accuracy, so it is not suitable for the new wind farms which are lack of historical data. This paper presented a wind power prediction method for power grid based on small sample set. Firstly, based on few historical data of wind farm, the general wind power prediction model was built with using support vector machine （SVM） method, and was used for the preliminary forecasting of wind power. Then, on the basis of the general model prediction, the characteristic parameters of regional wind farm were used to adjust and modify the general model for power grid, so as to obtain the prediction results of wind power for regional power grid. Finally, the practical examples verified the feasibility of prediction method based on small sample set. The results show that the prediction method based on small sample set with good accuracy is suitable for the power prediction of wind farm with few historical data, and can reduce the dependence of statistical prediction method on data during power forecasting.