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Short-Term Wind Speed Forecasting Using the Data Processing Approach and the Support Vector Machine Model Optimized by the Improved Cuckoo Search Parameter Estimation Algorithm
المؤلفون المشاركون
Wang, Chen
Hu, Zhongjin
Wang, Jianzhou
Wu, Jie
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2016، العدد 2016 (31 ديسمبر/كانون الأول 2016)، ص ص. 1-17، 17ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2016-07-19
دولة النشر
مصر
عدد الصفحات
17
التخصصات الرئيسية
الملخص EN
Power systems could be at risk when the power-grid collapse accident occurs.
As a clean and renewable resource, wind energy plays an increasingly vital role in reducing air pollution and wind power generation becomes an important way to produce electrical power.
Therefore, accurate wind power and wind speed forecasting are in need.
In this research, a novel short-term wind speed forecasting portfolio has been proposed using the following three procedures: (I) data preprocessing: apart from the regular normalization preprocessing, the data are preprocessed through empirical model decomposition (EMD), which reduces the effect of noise on the wind speed data; (II) artificially intelligent parameter optimization introduction: the unknown parameters in the support vector machine (SVM) model are optimized by the cuckoo search (CS) algorithm; (III) parameter optimization approach modification: an improved parameter optimization approach, called the SDCS model, based on the CS algorithm and the steepest descent (SD) method is proposed.
The comparison results show that the simple and effective portfolio EMD-SDCS-SVM produces promising predictions and has better performance than the individual forecasting components, with very small root mean squared errors and mean absolute percentage errors.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Wang, Chen& Wu, Jie& Wang, Jianzhou& Hu, Zhongjin. 2016. Short-Term Wind Speed Forecasting Using the Data Processing Approach and the Support Vector Machine Model Optimized by the Improved Cuckoo Search Parameter Estimation Algorithm. Mathematical Problems in Engineering،Vol. 2016, no. 2016, pp.1-17.
https://search.emarefa.net/detail/BIM-1112242
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Wang, Chen…[et al.]. Short-Term Wind Speed Forecasting Using the Data Processing Approach and the Support Vector Machine Model Optimized by the Improved Cuckoo Search Parameter Estimation Algorithm. Mathematical Problems in Engineering No. 2016 (2016), pp.1-17.
https://search.emarefa.net/detail/BIM-1112242
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Wang, Chen& Wu, Jie& Wang, Jianzhou& Hu, Zhongjin. Short-Term Wind Speed Forecasting Using the Data Processing Approach and the Support Vector Machine Model Optimized by the Improved Cuckoo Search Parameter Estimation Algorithm. Mathematical Problems in Engineering. 2016. Vol. 2016, no. 2016, pp.1-17.
https://search.emarefa.net/detail/BIM-1112242
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1112242
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر
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