| 引用本文: | 刘立阳,吴军基,孟绍良.短期风电功率预测误差分布研究[J].电力系统保护与控制,2013,41(12):65-70.[点击复制] |
| LIU Li-yang,WU Jun-ji,MENG Shao-liang.Research on error distribution of short-term wind power prediction[J].Power System Protection and Control,2013,41(12):65-70[点击复制] |
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| 摘要: |
| 短期风电功率预测一直是风电领域的研究热点，提出采用带位置和尺度参数的t分布描述风电功率预测的误差分布。分别采用差分自回归移动平均模型和BP神经网络，根据风电场实测数据进行功率预测，对两种预测模型产生的误差进行分析，验证了带位置和尺度参数的t分布可以有效描述预测误差分布。短期风电功率预测研究发现，带位置和尺度参数的t分布对误差的拟合优度高于正态分布，其各项参数可作为评价预测算法准确度的指标，通过分析分布参数可以直观了解预测算法的性能。 |
| 关键词: 风电功率预测 误差分布 带位置和尺度参数的t分布 差分自回归移动平均模型 BP神经网络 |
| DOI：10.7667/j.issn.1674-3415.2013.12.011 |
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| 基金项目:国家电网公司科技项目(2011LY226090423) |
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| Research on error distribution of short-term wind power prediction |
| LIU Li-yang,WU Jun-ji,MENG Shao-liang |
| () |
| Abstract: |
| Short-term wind power prediction is a popular issue in the research field. This paper proposes t location-scale distribution to describe the errors distribution of wind power prediction. Based on the measured data in wind power plants, autoregressive integrated moving average model and back propagation neural network are adopted to analyze the errors of two forecast models respectively, proving that the t location-scale distribution can describe effectively the frequency distribution of forecast errors, and the specific research data show that the goodness of fit of t location-scale distribution is better than that of normal distribution. Parameters of t location-scale distribution, as the indicators for judging the accuracy degree of the prediction algorithm, make it available to analyze its performance directly. |
| Key words: wind power prediction error distribution t location-scale distribution ARIMA BP neural network |
