Comparison between the bivariate Weibull probability approach and linear regression for assessment of the long-term wind energy resource using MCP
Comparison between the bivariate Weibull probability approach and linear regression for assessment of the long-term wind energy resource using MCP
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DOI：
10.1016/j.renene.2014.02.020
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发表时间：
2014-08
期刊：
影响因子：
8.7
通讯作者：
S. Weekes;A. Tomlin
中科院分区：
文献类型：
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作者：
S. Weekes;A. Tomlin
对基于相关站点风速双变量威布尔概率分布的测量-相关-预报(MCP)方法进行了详细的研究。由于风速通常被假定为服从威布尔分布，因此该方法比广泛使用的回归MCP技术具有更强的理论基础。在将这项技术应用于人工生成的风数据的先前工作的基础上，我们使用了22对相关英国站点的长期(11年)风观测。此外，22个人工风数据集是根据22个站点对的观测数据模拟的理想BW分布生成的。对拟合效率的比较表明，由于季节变化，从观测数据中准确提取BW分布参数所需的数据周期比人工风数据要长得多。使用22个站点对1-12个月的多个短期测量期的观测和人工生成的风数据，将BW方法的总体性能与标准回归MCP技术进行比较，以预测10年的风资源。通过比较每个站点的平均风速、平均风功率密度、威布尔形状因子和风速标准差的预测值和观测值来量化预测误差。使用人工风数据，BW方法在所有测量期内的表现都优于回归方法。然而，当应用于实际风速观测时，当使用完整的12个月测量周期时，BW方法的性能与回归方法相当，而对于较短的数据周期，BW方法的性能通常比回归方法差。这表明，相关地点的实际风场观测可能与理想的BW分布不同，因此回归方法可能更合适，因为回归方法需要较少的拟合参数，特别是在使用较短的测量周期时。
A detailed investigation of a measure–correlate–predict (MCP) approach based on the bivariate Weibull (BW) probability distribution of wind speeds at pairs of correlated sites has been conducted. Since wind speeds are typically assumed to follow Weibull distributions, this approach has a stronger theoretical basis than widely used regression MCP techniques. Building on previous work that applied the technique to artificially generated wind data, we have used long-term (11 year) wind observations at 22 pairs of correlated UK sites. Additionally, 22 artificial wind data sets were generated from ideal BW distributions modelled on the observed data at the 22 site pairs. Comparison of the fitting efficiency revealed that significantly longer data periods were required to accurately extract the BW distribution parameters from the observed data, compared to artificial wind data, due to seasonal variations. The overall performance of the BW approach was compared to standard regression MCP techniques for the prediction of the 10 year wind resource using both observed and artificially generated wind data at the 22 site pairs for multiple short-term measurement periods of 1–12 months. Prediction errors were quantified by comparing the predicted and observed values of mean wind speed, mean wind power density, Weibull shape factor and standard deviation of wind speeds at each site. Using the artificial wind data, the BW approach outperformed the regression approaches for all measurement periods. When applied to the real wind speed observations however, the performance of the BW approach was comparable to the regression approaches when using a full 12 month measurement period and generally worse than the regression approaches for shorter data periods. This suggests that real wind observations at correlated sites may differ from ideal BW distributions and hence regression approaches, which require less fitting parameters, may be more appropriate, particularly when using short measurement periods.