基于数理统计的短期风速预测修正方法

摘要
图/表
参考文献
相关文章 (15)

全文: PDF (987 KB) HTML (1 KB)
输出: BibTeX | EndNote (RIS)

摘要准确的短期风速预测, 可以有效地减轻风电场对电力系统的不利影响, 提高风电场在电力市场中的竞争力。由于风速具有很强的随机性和波动性, 常规的风速预测方法在风速突变点均存在较大的预测误差, 且通过改善预测方法本身无法得到有效地解决。本文从预测数据后期修正的角度出发, 结合数理统计思想, 提出了一种基于历史风速数据波动特性及一定置信水平的短期风速预测修正方法, 将原始风速预测值进行修正处理, 得到优化风速预测值。该方法具有通用性, 可以应用于不同方法风速预测结果的修正。对基于灰色GM(1, 1)模型的风速预测结果进行修正, 验证了该方法的有效性和可行性。

	服务

	把本文推荐给朋友
	加入我的书架
	加入引用管理器
	E-mail Alert
	RSS
	作者相关文章
	王世谦
	田春筝
	黄景慧

关键词 ：风速预测, 波动特性, 数理统计, 数据修正, 风力发电

Abstract：To accurately forecast short-term wind speed can effectively reduce the adverse effects of wind power in power systems and improve the competition of wind power in power markets. Because of the strong randomicity of wind, the forecasting method at wind mutation points are greatly improved, and through improve the forecasting method itself cannot obtain more favorable effect. From the angle of revision, a revision method was proposed and applied to revision the forecast wind speed in this paper, which based on the historical data wave characteristics and confidence level. Under this method, we can turned the original forecast wind speed into the optimized forecast wind speed. The revision method applies to all short-term wind prediction methods. The validity and feasibility of the method was verified through the real example of a wind speed forecasting method based on the Grey model.

Key words： wind speed forecast wave characteristics mathematics statistics data revision wind power

收稿日期: 2014-06-26 出版日期: 2014-01-23

作者简介: 王世谦(1988-), 男, 河南濮阳人, 硕士, 主要研究方向为电力系统分析、电网规划。

引用本文:

王世谦, 田春筝, 黄景慧 . 基于数理统计的短期风速预测修正方法[J]. 电气技术, 2013, 14(11): 11-15. Wang Shiqian, Tian Chunzheng, Huang Jinghui. Correction Method for Short Term Wind Speed Forecasting Results Based on Mathematical Statistics. Electrical Engineering, 2013, 14(11): 11-15.

链接本文:

https://dqjs.cesmedia.cn/CN/Y2013/V14/I11/11

[1] 杨茂, 熊昊, 严干贵, 等.基于数据挖掘和模糊聚类的风电功率实时预测研究[J].电力系统保护与控制, 2013, 41(1):1-6.
[2] 谷国利, 王维庆, 张新燕, 等.风电场风速预测方法的研究[J].农业工程学报:新能源产业, 2009(6):22-24.
[3] 卿湘运, 杨富文, 王行愚.采用贝叶斯–克里金–卡尔曼模型的多风电场风速短期预测[J].中国电机工程学报, 2012, 32(35):107-114.
[4] TORRES J L, GARCIA A, BLAS M D, et a1. Forecast of hourly average wind speed with ARMA models in navarre(Spain)[J].Solar Energy, 2005, 79:65-77.
[5] 张宏宇, 印永华, 申洪, 等.基于概率测度变换的风速时间序列建模方法[J].电力系统自动化, 2013, 37(2):7-10.
[6] BARBOUNIS T G, THEOCHARIS J B, ALEXIADIS M C, et a1.Long-term wind speed and power forecasting using local recurrent neural network Models[J]. Transactions on energy conversion, 2006, 21(1): 273-284.
[7] 黄小华, 李德源, 吕文阁.基于人工神经网络模型的风速预测[J].太阳能学报, 2011, 32(2):193-197.
[8] ALEXIADIS M, DOKOPOULOS P, SAHSAMANOGLOU H, et a1.Short term forecasting of wind speed and related electrical power[J].Solar Energy, 1998, 63(1): 61-68.
[9] BARBOUNIS T G, THEOCHARIS J B. A locally recurrent fuzzy neural network with application to the wind speed prediction using spatial correlation[J]. Neurocomputing, 2007, 70(15):25–42.
[10] 郭虎全, 刘吉臻, 柳玉, 等.基于小波包分析的风速预测研究[J].华东电力, 2011, 39(12): 2077-2079
[11] 罗文, 王莉娜.基于小波分解与遗传算法和支持向量机的短期风速预测[J].太阳能学报, 2012, 33(8): 1327- 1333.
[12] 刘辉, 田红旗, 李燕飞.基于小波分析法与滚动式时间序列法的风电场风速短期预测优化算法[J].中南大学学报(自然科学版), 2010, 41(1):370–375.
[13] 蔡凯, 谭伦农, 等.时间序列与神经网络法相结合的短期风速预测[J].电网技术, 2008, 32(8):82-85.
[14] 杨秀媛, 肖洋, 陈树勇.风电场风速和发电功率预测研究[J].中国电机工程学报, 2005, 25(11):1-5.
[15] 杜松怀, 温步瀛, 蒋传文.电力市场[M].3版.北京:中国电力出版社, 2008:107-108.