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A New Hybrid Approach of Clustering Based Probabilistic Decision Tree to Forecast Wind Power on Large Scales
Journal of Electrical Engineering & Technology ( IF 1.6 ) Pub Date : 2021-01-04 , DOI: 10.1007/s42835-020-00616-1
Mansoor Khan , Chuan He , Tianqi Liu , Farhan Ullah

The wind power forecasting plays a vital role in renewable energy production. Due to the dynamic and uncertain behavior of wind, it is really hard to catch the actual features of wind for accurate forecasting measures. The patchy and instability of wind leading to the assortment of training samples have a main influence on the forecasting accuracy. For this purpose, an accurate forecasting method is needed. This paper proposed a new hybrid approach of clustering based probabilistic decision tree to forecast wind power efficiently. The collected data is screened for noisy information and selected those variables which mainly contribute in accurate predictions. Then, the wind data is normalized using mean and standard deviation to extract playing level fields for each feature. Based on the similarity of the data behavior, the K-means clustering algorithm is applied to classify the samples into different groups which contain the historical wind data. Further, the Naïve Bayes (NB) tree is proposed to extract probabilities for each feature in the clusters. The NB tree is a hybrid model of C4.5 and NB methods that successfully applied on three big real-world wind datasets (hourly, monthly, yearly) collected from National Renewable Energy Laboratory (NREL). The forecasting accuracy exposed that the proposed method could forecast an accurate wind power from hours to years' data. Comprehensive comparisons are made of the proposed method with the most popular state of the art techniques which show that this method provides more accurate prediction results.



中文翻译:

一种基于聚类的概率决策树混合预测大规模风能的新方法

风力发电预测在可再生能源生产中起着至关重要的作用。由于风的动态性和不确定性,因此很难准确掌握风的实际特征以进行准确的预测。风的不连续性和不稳定性导致训练样本的种类对预测准确性有主要影响。为此,需要一种精确的预测方法。本文提出了一种新的基于概率决策树聚类的混合方法来有效地预测风电功率。筛选收集到的数据中是否包含嘈杂的信息,并选择那些主要有助于准确预测的变量。然后,使用均值和标准差对风数据进行归一化,以提取每个功能的游戏水平字段。根据数据行为的相似性,应用K均值聚类算法将样本分为包含历史风数据的不同组。此外,提出了朴素贝叶斯(NaïveBayes,NB)树来提取聚类中每个特征的概率。NB树是C4.5和NB方法的混合模型,已成功应用于从国家可再生能源实验室(NREL)收集的三个大型现实世界风数据集(每小时,每月,每年)。预测准确性暴露了所提出的方法可以预测从数小时到数年的准确风力。对提出的方法与最流行的最新技术进行了综合比较,结果表明该方法提供了更准确的预测结果。提出了朴素贝叶斯(NaïveBayes,NB)树来提取聚类中每个特征的概率。NB树是C4.5和NB方法的混合模型,已成功应用于从国家可再生能源实验室(NREL)收集的三个大型现实世界风数据集(每小时,每月,每年)。预测准确性暴露了所提出的方法可以预测从数小时到数年的准确风力。对提出的方法与最流行的最新技术进行了综合比较,结果表明该方法提供了更准确的预测结果。提出了朴素贝叶斯(NaïveBayes,NB)树来提取聚类中每个特征的概率。NB树是C4.5和NB方法的混合模型,已成功应用于从国家可再生能源实验室(NREL)收集的三个大型现实世界风数据集(每小时,每月,每年)。预测准确性暴露了所提出的方法可以预测从数小时到数年的准确风力。对提出的方法与最流行的最新技术进行了综合比较,结果表明该方法提供了更准确的预测结果。每年)从国家可再生能源实验室(NREL)收集。预测准确性暴露了所提出的方法可以预测从数小时到数年的准确风力。对提出的方法与最流行的最新技术进行了综合比较,结果表明该方法提供了更准确的预测结果。每年)从国家可再生能源实验室(NREL)收集。预测准确性暴露了所提出的方法可以预测从数小时到数年的准确风力。对提出的方法与最流行的最新技术进行了综合比较,结果表明该方法提供了更准确的预测结果。

更新日期:2021-01-04
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