| 基于改进灰色模型与BP神经网络模型组合的风力发电量预测研究 |
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| 引用本文： | 孙 佳,王 淳,胡 蕾.基于改进灰色模型与BP神经网络模型组合的风力发电量预测研究[J].水电能源科学,2015,33(4):203-205. |
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| 作者姓名： | 孙 佳 王 淳 胡 蕾 |
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| 作者单位： | 1. 南昌大学 信息工程学院， 江西 南昌 330031； 2. 国网江西省电力科学研究院， 江西 南昌 330096 |
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| 基金项目： | 国家自然科学基金项目(51167012)；江西省教育厅科技计划项目（GJJ14269，GJJ14165）；江西省博士后科研择优资助项目（2014KY26） |
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| 摘 要： | 针对灰色模型在数据序列无规律的风力发电量预测中精度不高的问题,通过对原始数据的平滑处理改进灰色模型,并将改进的灰色模型与BP神经网络相结合构建组合预测模型,采用灰色关联法改进组合预测的权重系数。实例分析表明,改进的优选组合模型预测的准确度高于单一模型及传统的优选组合预测模型。
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| 关 键 词： | 风力发电量预测 改进的灰色模型 BP神经网络模型 改进的优选组合预测 |
Research on Wind Power Generation Prediction Based on Combination of Improved Grey Model and BP Neural Network |
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| SUN Jia;WANG Chun;HU Lei.Research on Wind Power Generation Prediction Based on Combination of Improved Grey Model and BP Neural Network[J].International Journal Hydroelectric Energy,2015,33(4):203-205. |
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| Authors: | SUN Jia;WANG Chun;HU Lei |
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| Affiliation: | SUN Jia;WANG Chun;HU Lei;Information Engineering School,Nanchang University;State Grid Jiangxi Electric Power Research Institute; |
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| Abstract: | As the prediction accuracy of grey model for wind power generation with erratic presence of the original data was not high enough, grey model was improved by using smoothing techniques to handle the original data. The improved grey model and BP neural network were combined to construct a combination forecasting model, and grey relational analysis was adopted to improve the calculation of weighting coefficient in combination forecasting model. The example results show that the prediction accuracy of the combination forecasting model is higher than that of single forecasting model and traditional combination forecasting model. |
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| Keywords: | wind power generation forecasting improved grey model BP neural network improved optimized combination prediction |
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