理工学校的高级工程师桑丘萨尔塞多解释说，我们开发目的是用于预测风速的混合方法，就是利用风力发电机发电，用作可再生能源。
为了开发新的模型，科学家们利用由美国国家环境预报中心所提供的全球预报系统资料，根据这些数据使这个系统覆盖了整个地球，在互联网上免费提供分辨率为约100公里的服务。
这样研究人员能够进行更详细的预测，将所谓的来自美国大气研究中心的第五代中尺度模式（MM5模式）的预测系统用于加强覆盖范围，使其达到15x15公里。
萨尔塞多解释说，这些信息当然是不够的，它只可以预测某个特定风力发电机的风速，这就是为什么我们要采用人工神经网络来预测风速。对于这些网络的学习和自动资料系统的处理，是模拟动物神经系统的运作。在这种情况下，他们使用的温度，气压和所提供的风速数据就是预测模型，用以收集风力发电机本身的数据。
有了这些数据，一旦该系统已被用于预测，风速将在1至48小时之内测定。风力发电场必须依法将这些预测数据发送到提供电力和运行系统的红电气西班牙公司。
萨尔塞多说，该方法可立即应用，如果可以预测风速的一个风力，那么我们就可以估算它将会产生多少能量。因此，通过飞机，我们可以预测整个风电场产生的能量。该方法已经被成功地应用在阿尔巴塞特风电场。
研究人员正在继续改进方法，并于最近发表的一篇文章，不只是单一模式，提出了利用全球性预测模型，改进神经网络。其结果是，小组的意见得到了证实，然后应用到银行的神经网络中，以实现更精确的预测风力风速。
萨尔塞多说，所获得的结果显示，与以往的模式相比，预测的结果改善了2%。虽然这可能看起来是一个小小的改善，但实际上确是很大的改善，因为我们是正在谈论关于提高预测生产能源的价值，这个价值可以达到数百万欧元。
附英语原文：
Neural networks used to improve wind speed forecasting
The aim of the hybrid method we have developed is to predict the wind speed in each of the aerogenerators in a wind farm,' explained Sancho Salcedo, an engineer at the Escuela Politecnica Superior and co-author of the study, published on-line in the journal Renewable Energy.
In order to develop the new model, the scientists used information provided by the Global Forecasting System from the US National Centres for Environmental Prediction. The data from this system cover the entire planet with a resolution of approximately 100 kilometres and are available for free on the internet.
Researchers are able to make more detailed predictions by integrating the so-called 'fifth generation mesoscale model (MM5), from the US National Centre of Atmospheric Research, designed to enhance resolution to 15x15 kilometres.
'This information is still not enough to predict the wind speed of one particular aerogenerador, which is why we applied artificial neural networks,' Salcedo clarified. These networks are automatic information learning and processing systems that simulate the workings of animal nervous systems. In this case, they use the temperature, atmospheric pressure and wind speed data provided by forecasting models, as well as the data gathered by the aerogenerators themselves.
With these data, once the system has been 'trained,' predictions regarding wind speed will be made between one and 48 hours in advance. Wind farms are obliged by law to supply these predictions to Red Electrica Espanola, the company that delivers electricity and runs the Spanish electricity system.
Salcedo says the method can be applied immediately: 'If the wind speed of one aerogenerator can be predicted, then we can estimate how much energy it will produce. Therefore, by summing the predictions for each 'aero,' we can forecast the production of an entire wind farm.' The method has already been used very successfully at the wind farm in Fuentasanta, in Albacete.
Researchers are continuing to improve the method and recently proposed the use of several global forecasting models instead of just one, according to an article published this year in Neurocomputing. As a result, several sets of observations are obtained, which are then applied to banks of neural networks to achieve a more accurate prediction of aerogenerator wind speeds.
The results obtained reveal an improvement of 2% in predictions compared to the previous model. 'Although this may seem like a small improvement, it is really substantial, as we are talking about an improvement in predicting energy production that could be worth millions of euros, Salcedo concluded.
（原文出处：http://www.sciencecentric.com/news/article.php?q=09050114-neural-networks-used-improve-wind-speed-forecasting）
