Using artificial neural networks for temporal and spatial wind speed forecasting in Iran
Using artificial neural networks for temporal and spatial wind speed forecasting in Iran
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DOI：
10.1016/j.enconman.2016.02.041
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发表时间：
2016-05
影响因子：
10.4
通讯作者：
Y. Noorollahi;M. Jokar;A. Kalhor
中科院分区：
文献类型：
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作者：
Y. Noorollahi;M. Jokar;A. Kalhor
在过去的几年里，世界范围内的风力发电取得了重大进展。由于风速的湍流性质，风的间歇性管理是风能领域的一个重要研究领域。本文从两个方面对这一问题进行了研究，即利用人工神经网络(ANN)在时间维度和空间维度上预测风速。人工神经网络是一种新的方法，适用于风速等复杂系统的建模，通常通过大量注册数据来研究，并举例说明了其行为。我们首先在伊朗的三个风站(WOSS)预测了一小时时间间隔的风速时间维度，作为短期风速预测。在接下来的部分中，利用附近其他气象中心的数据对气象气象中心的风速数据进行了估算。由于数据收集的局限性，本研究选择了两组WOSS作为研究对象。由两组最优模型得到的风速直方图误差平均值约为2.6%，这是有希望的。在伊朗，气象数据的稀缺导致了对风能资源的研究有限。因此，这种类型的空间预测在伊朗风能行业的风能资源评估中非常有用。这是一个有价值的工具，使决策者能够在调查的第一步就准确地检测整个地区的高风速地区。
Over the past few years, significant progress has been made in wind power generation worldwide. Because of the turbulent nature of wind velocity, the management of wind intermittence is a substantial field of research in the wind energy sector. This paper presents an investigation of this problem in two parts, the prediction of wind speed in both temporal and spatial dimensions, using artificial neural networks (ANNs). ANNs are novel methods applicable in modeling of complicated systems such as wind speed which generally investigated by a large amount of registered data exemplifying the behavior of.We first predicted the temporal dimension of wind speed at one-hour time interval, as a short-term wind speed prediction, in three wind observation stations (WOSs) in Iran. In the next part, estimation of wind speed data in a WOS using data from some other nearby WOSs was carried out. Due to the limitation of data collection, two groups of WOSs were selected for this target. The average value of the wind speed histogram error obtained from the best model in both groups is about 2.6% which is certainly promising.In Iran, the scarcity of meteorological data has resulted in the limited study of wind energy resources. Therefore, this type of spatial prediction is very useful in wind resource assessment in the Iranian wind energy industry. This is a valuable tool that enables the decision maker to precisely detect the high wind speed areas over an entire region in the first step of investigation.