基于模糊均生函数-最优子集回归的风电场风速预测
首发时间:2017-07-07
摘要:为进一步提高风电场短期风速预测精度,针对风速样本数据既有随机波动性又有趋势性的特点,提出一种基于模糊均生函数-最优子集回归(fuzzy mean generating function-optimal subset regression,FMGF-OSR)的短期风速预测模型。该模型利用逆推算法和模糊理论对均生函数进行了改进,构造出模糊均生函数,然后将模糊均生函数与最优子集回归算法相结合,建立短期风速预测模型。仿真结果表明:与传统预测方法相比,所提模型集合了均生函数和模糊理论以及逆推算法的优点,大大提高了预测模型的精度,表明了该模型的有效性和优越性。
关键词: 短期风速预测 模糊均生函数 最优子集回归 逆推算法
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Wind speed prediction based on fuzzy mean generating function-optimal subset regression of wind farms
Abstract:In order to further improve the accuracy of short-term wind speed prediction, aimed at the characteristics of random variation and tendency of wind speed sample sequences, a short-term wind speed prediction model based on fuzzy mean generating function-optimal subset regression (FMGF-OSR) is proposed in this paper. We firstly improve the calculation method of the MGF by back-stepping algorithm and fuzzy theory, and then combine it with the OSR algorithm to establish the short-term wind speed prediction model. In order to validate the validity and superiority of this model in the actual wind speed prediction, this paper also use the classical model like MGF-PCA(mean generating function-principal component analysis), MGF-OSR(mean generating function-optimal subset regression) and ARMA(auto-regressive moving average) for wind speed prediction. The simulation results show that the prediction model proposed in this paper can combine the advantages of the mean generating function, the fuzzy theory and the back-stepping algorithm, and can greatly improve the prediction accuracy, showing its validity and superiority.
Keywords: short-time wind speed prediction fuzzy mean generating function(FMGF) optimal subset regression(OSR) back-stepping algorithm
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