A new ANN-based methodology for very short-term wind speed prediction using Markov chain approach
A new ANN-based methodology for very short-term wind speed prediction using Markov chain approach
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DOI：
10.1109/epc.2008.4763386
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发表时间：
2008
期刊：
影响因子：
--
通讯作者：
S. Kani;G. Riahy
中科院分区：
文献类型：
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作者：
S. Kani;G. Riahy
自2000年以来，世界各地（主要是欧洲和美国）风电装机容量的增加引起了电力公司、风电场推动者和研究人员对短期预测的关注，主要是由于越来越多的未知（波动）风电需要并入电网。此外，在一个放松管制的系统中，有效交易、充分利用输电线路能力、解决系统频率问题、准确的短期预测比以往任何时候都更有动力。本研究将人工神经网络（ANN）与马尔可夫链方法相结合，发展极短期风速预报。人工神经网络预测短期值，并应用马尔可夫链根据长期模式对结果进行修正。为了验证，将该方法与人工神经网络进行了比较。结果表明了该综合方法的有效性。
Since year 2000, the increase of the installed wind energy capacity all over the world (mainly in Europe and United States) attracted the attention of electricity companies, wind farm promoters and researchers towards the short term prediction, mainly motivated by the necessity of integration into the grid of an increasing dasiaunknownpsila (fluctuating) amount of wind power. Besides, in a deregulated system, the ability to trade efficiently, make the best use of transmission line capability and address concerns with system frequency, accurate very short-term forecasts are motivated more than ever. In this study, very short term wind speed forecasting is developed utilizing artificial neural networks (ANN) in conjunction with Markov chain approach. Artificial neural networks predict short term values and the results are modified according to the long term patterns due to applying Markov chain. For verification purposes, the integrated proposed method is compared with ANN. The results show the effectiveness of the integrated method.