Due to the low dispatchability of wind power, the massive integration of this energy source in power systems requires short-term and very short-term wind power output forecasting models to be as efficient and stable as possible. A study is conducted in the present paper of potential improvements to the performance of artificial neural network (ANN) models in terms of efficiency and stability. Generally, current ANN models have been developed by considering exclusively the meteorological information of the wind farm reference station, in addition to selecting a fixed number of time periods prior to the forecasting. In this respect, new ANN models are proposed in this paper, which are developed by: varying the number of prior $1-h$ periods (periods prior to the forecasting hour) chosen for the input layer parameters; and/or incorporating in the input layer data from a second weather station in addition to the wind farm reference station. It has been found that the model performance is always improved when data from a second weather station are incorporated. The mean absolute relative error (MARE) of the new models is reduced by up to 7.5%. Furthermore, the longer the forecasting horizon, the greater the degree of improvement.

中文翻译：

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人工神经网络模型在风电场发电量短期预测中的性能改进

由于风能的可调度性低，这种能源在电力系统中的大规模集成要求短期和非常短期的风能输出预测模型要尽可能高效和稳定。本文针对效率和稳定性方面的潜在改进对人工神经网络（ANN）模型的性能进行了研究。通常，除了在预测之前选择固定数量的时间段以外，还通过仅考虑风电场参考站的气象信息来开发当前的ANN模型。在这方面，本文提出了新的人工神经网络模型，其开发方法是：改变先验数目$ 1-小时$为输入层参数选择的时间段（预测时间之前的时间段）；和/或将来自风场参考站之外的第二气象站的数据合并到输入层中。已经发现，当合并来自第二气象站的数据时，模型性能总是得到改善。新模型的平均绝对相对误差（MARE）降低了7.5％。此外，预测范围越长，改进程度就越大。