Wind energy is one of the most promising alternatives as energy sources; however, to obtain the best results, producers need to forecast the wind speed, generated power and energy price in order to provide the appropriate tools for optimal operation, planning, control and marketing both for isolated wind systems and for those that are interconnected to a main distribution network. For the present work, a novel methodology is proposed for the forecasting of time series in wind energy systems; it consists of a high-order neural network that is trained on-line by the extended Kalman filter algorithm. Unlike most modern artificial intelligence methods of forecasting, which are based on hybridizations, data pre-filtering or deep learning methods, the proposed method is based on the simplicity of implementation, low computational complexity and real-time operation to produce 15-step-ahead forecasting in a time series of wind speed, generated power and energy price. The proposed scheme has been evaluated using real data from open access repositories of wind farms. The results show that an on-line training of the neural network produces high precision, without the need for any other information beyond a few past observations.

中文翻译：

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基于高阶神经网络的风能系统时间序列预测

风能是最有前途的替代能源之一。但是，为了获得最佳结果，生产商需要预测风速，发电量和能源价格，以便为孤立的风能系统以及与风能系统互联的系统提供最佳运行，规划，控制和营销的适当工具。主分销网络。对于当前的工作，提出了一种新的方法来预测风能系统中的时间序列。它由一个高级神经网络组成，该神经网络通过扩展的卡尔曼滤波算法进行在线训练。与基于混合，数据预过滤或深度学习方法的大多数现代人工智能预测方法不同，该方法基于实现的简单性，低的计算复杂度和实时操作可按风速，发电量和能源价格的时间序列进行15步超前预报。使用来自风电场开放存取存储库的真实数据对提议的方案进行了评估。结果表明，对神经网络的在线训练可以产生很高的精度，而无需其他一些过去观察到的信息。