| 引用本文: | 乔梁,张露,许懿,等.基于最大-最小贴近度和诱导有序加权算子的
风电功率短期预测模型[J].电力系统保护与控制,2014,42(19):114-121.[点击复制] |
| QIAO Liang,ZHANG Lu,XU Yi,et al.Wind power short-term forecast model based on maximum-minimum approach degree and induced ordered weighted operator[J].Power System Protection and Control,2014,42(19):114-121[点击复制] |
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| 摘要: |
| 为了提高风电功率短期预测精度，将最大-最小贴近度和诱导有序加权算子相结合，提出了一种新的组合模型。根据诱导有序加权算子的不同，可形成不同的组合模型，即IOWA组合模型、IOWHA组合模型和IOWGA组合模型。由于预测期的实际值未知，各单项预测模型的诱导值无法提前预知，不能直接利用该方法进行预测。利用各单项模型建立不同组合模型，选择精度较高的组合模型，用其预测值代替实际值计算诱导值，可以解决预测期诱导值的计算问题。两个不同风电场的仿真结果表明：IOWGA组合模型比某些单项模型和其他组合模型的预测精度还低，预测效果并未得到改善；IOWA组合模型和IOWHA组合模型的各项误差指标都小于单项模型和其他组合模型，预测精度都得到提高，但IOWHA组合模型的各项预测评价指标都最好，预测精度更高，将它的预测值作为风电功率最终预测值，能提高风电功率预测精度。 |
| 关键词: 风电功率 最大-最小贴近度 IOWA组合模型 IOWHA组合模型 IOWGA组合模型 |
| DOI：10.7667/j.issn.1674-3415.2014.19.018 |
| 投稿时间：2013-12-26修订日期：2014-03-05 |
| 基金项目:输配电装备及系统安全与新技术国家重点实验室自主研究项目(2007DA10512712205) |
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| Wind power short-term forecast model based on maximum-minimum approach degree and induced ordered weighted operator |
| QIAO Liang,ZHANG Lu,XU Yi,LIANG Wei,SUN Lu,LU Ji-ping |
| (Chongqing Electric Power Dispatching Control Center, Chongqing 400000, China;State Key Laboratory of Power Transmission Equipment & System Security and New Technology (Chongqing University), Chongqing 400044, China;Chongqing Electric Power Design Institute, Chongqing 401120, China) |
| Abstract: |
| In order to improve the accuracy of wind power short-term forecast, a kind of new combination forecasting model is proposed combining the maximum-minimum approach degree with induced ordered weighted operator. According to different induced ordered weighted operators, different combination forecasting models can be constructed, namely IOWA combination model, IOWHA combination model and IOWGA combination model. Because real data are unknown in the forecast period and induced value of every single forecasting method can’t be calculated in advance, this method can not be used to predict directly. Forecasting value of the highest accuracy combination forecasting model selected from different combination models built by single forecasting models instead of real data can solve the induced value calculation. Simulation results of two different wind power farms show that compared with some single forecasting models and another combination models, the forecast precision of IOWGA combination model is lower and predictive effect isn’t improved; forecasting error indexs of IOWA combination model and IOWHA combination model are all lower and forecast precision of those is higher, moreover, predictive effect of IOWHA combination model is better than IOWA combination model, therefore, taking forecast value of IOWHA combination model as final prediction result can effectively improve the prediction accuracy of wind power. |
| Key words: wind power forecast maximum-minimum approach degree IOWA combination model IOWHA combination model IOWGA combination model |
