Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China
Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China
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利用混合模型对中国新疆三个不同地点进行中期风速预测
DOI：
10.1016/j.renene.2014.11.011
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发表时间：
2015-04
期刊：
影响因子：
8.7
通讯作者：
Haiyan Jiang
中科院分区：
文献类型：
--
作者：
Jianzhou Wang;Shanshan Qin;Qingping Zhou;Haiyan Jiang
由于全球能源依赖性和利用化石燃料的有害环境影响，对可再生和清洁能源的兴趣变得越来越大。因此，风能作为世界上最有前途的绿色能源之一，受到越来越多的关注。由于风速影响电网运行调度、风力发电和风电场规划，风速预测变得越来越重要。为了提高风速预测的性能，已经进行了许多研究。然而，较少的工作已经执行，以预处理存在于原始风速数据中的异常值，以实现准确的预测。本文将支持向量回归机（SVR）与季节指数调整（SIA）和Elman递归神经网络（ERNN）方法相结合，成功地构建了混合模型PMERNN和PAERNN。然后，本文提出了一个中期的风速预报性能分析，在中国新疆地区的三个不同的网站，利用每天的风速数据收集了8年。实验结果表明，与其他模型相比，混合模型在预测范围内具有更高的精度来预测每日风速。
Interest in renewable and clean energy sources is becoming significant due to both the global energy dependency and detrimental environmental effects of utilizing fossil fuels. Therefore, increased attention has been paid to wind energy, one of the most promising sources of green energy in the world. Wind speed forecasting is of increasing importance because wind speeds affect power grid operation scheduling, wind power generation and wind farm planning. Many studies have been conducted to improve wind speed prediction performance. However, less work has been performed to preprocess the outliers existing in the raw wind speed data to achieve accurate forecasting. In this paper, Support Vector Regression (SVR), a learning machine technique for detecting outliers, has been successfully combined with seasonal index adjustment (SIA) and Elman recurrent neural network (ERNN) methods to construct the hybrid models named PMERNN and PAERNN. Then, this paper presents a medium-term wind speed forecasting performance analysis for three different sites in the Xinjiang region of China, utilizing daily wind speed data collected over a period of eight years. The experimental results suggest that the hybrid models forecast the daily wind velocities with a higher degree of accuracy over the prediction horizon compared to the other models.
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影响因子：
4.9
作者：
Barbounis, TG;Theocharis, JB;Dokopoulos, PS
通讯作者：
Dokopoulos, PS
DOI：
10.1109/epc.2008.4763386
发表时间：
2008
期刊：
2008 IEEE Canada Electric Power Conference
影响因子：
--
作者：
S. Kani;G. Riahy
通讯作者：
S. Kani;G. Riahy
影响因子：
8.7
作者：
Pan Zhao;Jiangfeng Wang;Junrong Xia;Yiping Dai;Yingxin Sheng;Jie-shun Yue
通讯作者：
Pan Zhao;Jiangfeng Wang;Junrong Xia;Yiping Dai;Yingxin Sheng;Jie-shun Yue
DOI：
10.1109/pes.2006.1709479
发表时间：
2006-10
期刊：
2006 IEEE Power Engineering Society General Meeting
影响因子：
--
作者：
M. Miranda;R. Dunn
通讯作者：
M. Miranda;R. Dunn
DOI：
--
发表时间：
1996-12
期刊：
--
影响因子：
--
作者：
V. Vapnik;S. Golowich;Alex Smola
通讯作者：
V. Vapnik;S. Golowich;Alex Smola