The need for an efficient power source for operating the modern industry has been rapidly increasing in the past years. Therefore, the latest renewable power sources are difficult to be predicted. The generated power is highly dependent on fluctuated factors (such as wind bearing, pressure, wind speed, and humidity of surrounding atmosphere). Thus, accurate forecasting methods are of paramount importance to be developed and employed in practice. In this paper, a case study of a wind harvesting farm is investigated in terms of wind speed collected data. For data like the wind speed that are hard to be predicted, a well built and tested forecasting algorithm must be provided. To accomplish this goal, four neural network-based algorithms: artificial neural network (ANN), convolutional neural network (CNN), long short-term memory (LSTM), and a hybrid model convolutional LSTM (ConvLSTM) that combines LSTM with CNN, and one support vector machine (SVM) model are investigated, evaluated, and compared using different statistical and time indicators to assure that the final model meets the goal that is built for. Results show that even though SVM delivered the most accurate predictions, ConvLSTM was chosen due to its less computational efforts as well as high prediction accuracy.

中文翻译：

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使用基于人工学习的算法进行短时风速预测。

在过去的几年中，对运行现代工业的高效电源的需求迅速增长。因此，最新的可再生能源很难预测。产生的功率高度依赖于波动因素（例如风向，压力，风速和周围大气的湿度）。因此，准确的预测方法在实践中至关重要。本文以风速收集数据为例，研究了一个风电场的案例。对于像风速这样的难以预测的数据，必须提供经过精心设计和测试的预测算法。为了实现这一目标，我们使用了四种基于神经网络的算法：人工神经网络（ANN），卷积神经网络（CNN），长短期记忆（LSTM）以及将LSTM与CNN相结合的混合模型卷积LSTM（ConvLSTM）和一个支持向量机（SVM）模型，使用不同的统计和时间指标进行研究，评估和比较，以得出确保最终模型满足构建的目标。结果表明，即使SVM提供了最准确的预测，仍选择ConvLSTM是因为其计算工作量少且预测准确性高。