基于修正后ARIMA-GARCH模型的超短期风速预测

发布时间：2018-01-13 02:33

本文关键词：基于修正后ARIMA-GARCH模型的超短期风速预测　出处：《电网技术》2017年06期 　论文类型：期刊论文

【摘要】：精准的风速预测对于降低风功率波动对电网稳定性的影响具有重大意义。为此,基于时间序列分析方法中的自回归差分移动平均模型(auto regressive integrated moving average,ARIMA)和广义自回归条件异方差模型(generalized autoregressive conditionally heteroscedastic,GARCH),对风速建立ARIMA-GARCH模型。并从实际经验出发,充分考虑风速自身高低及风速变化率对条件方差的影响,利用模糊理论、风速概率分布特性及相关性分析等方法,对GARCH模型中的条件方差进行加权混合修正,使其更加符合实际情况。此方法预测机理清楚,能够反映客观世界动态过程,揭示风速变化的客观规律,并且,仅通过少量历史数据,便可快速实现超短期内风速的点预测与区间预测。最后,通过中国甘肃气象站观测的风速数据进行了详细的算例分析,验证了上述方法的可行性及有效性。
[Abstract]:Accurate wind speed prediction is of great significance for reducing the impact of wind power fluctuation on power grid stability. Auto regressive integrated moving average based on autoregressive differential moving average model in time series analysis. Arima and generalized autoregressive conditional heteroscedasticity model. Generalized autoregressive conditionally heteroscedastic. ARIMA-GARCH model is established for wind speed. Based on practical experience, the influence of wind speed itself and wind speed change rate on conditional variance is fully considered, and fuzzy theory is used. The probability distribution characteristics of wind speed and the correlation analysis are used to modify the conditional variance in GARCH model to make it more in line with the actual situation. The prediction mechanism of this method is clear. It can reflect the dynamic process of the objective world, reveal the objective law of wind speed change, and, through a small amount of historical data, can quickly realize the point prediction and interval prediction of the wind speed in the ultra-short term. The feasibility and effectiveness of the above method are verified by a detailed analysis of the wind speed data of Gansu Meteorological Station in China.
【作者单位】：电力传输与功率变换控制教育部重点实验室(上海交通大学电子信息与电气工程学院);
【基金】：国家自然科学基金项目(51007058,51477097,51677115)~~
【分类号】：TM614
【正文快照】： 示风速变化的客观规律,并且,仅通过少量历史数据,便可快速实现超短期内风速的点预测与区间预测。最后,通过中国甘肃气象站观测的风速数据进行了详细的算例分析,验证了上述方法的可行性及有效性。0引言随着世界气候变化的影响,以及全球能源结构的调整,以风电为代表的可再生能源

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