The share of wind power in fuel mixes worldwide has increased considerably. The main ingredient when deriving wind power predictions are wind speed data; the closer to the wind farms, the better they forecast the power supply. The current paper proposes a hybrid model for predicting wind speeds at convenient locations. It is then applied to Southern California power price area. We build random fields with time series of gridded historical forecasts and actual wind speed observations. We estimate with ordinary kriging the spatial variability of the temporal parameters and derive predictions. The advantages of this work are twofold: (1) an accurate daily wind speed forecast at any location in the area and (2) a general method applicable to other markets.

翻译：风能在全世界燃料混合中所占的份额已大为增加。在得出风力预测时,主要成分是风速数据;风力农场越近,对电力供应的预测越好。本文提出了一个在方便地点预测风速的混合模型,然后应用于南加州电价区。我们用时间序列的网格历史预测和实际风速观测来建立随机场。我们用普通的轮廓来估计时间参数的空间变异性并作出预测。这项工作的优势有两个方面:(1) 准确的每日风速预报,以及(2) 适用于其他市场的一般方法。