Short-Term Wind Speed Forecasting Based on Ensemble Online Sequential Extreme Learning Machine and Bayesian Optimization
المؤلفون المشاركون
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2020، العدد 2020 (31 ديسمبر/كانون الأول 2020)، ص ص. 1-13، 13ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2020-11-29
دولة النشر
مصر
عدد الصفحات
13
التخصصات الرئيسية
الملخص EN
Short-term wind speed forecasting is crucial to the utilization of wind energy, and it has been employed widely in turbine regulation, electricity market clearing, and preload sharing.
However, the wind speed has inherent fluctuation, and accurate wind speed prediction is challenging.
This paper aims to propose a hybrid forecasting approach of short-term wind speed based on a novel signal processing algorithm, a wrapper-based feature selection method, the state-of-art optimization algorithm, ensemble learning, and an efficient artificial neural network.
Variational mode decomposition (VMD) is employed to decompose the original wind time-series into sublayer modes.
The binary bat algorithm (BBA) is used to complete the feature selection.
Bayesian optimization (BO) fine-tuned online sequential extreme learning machine (OSELM) is proposed to forecast the low-frequency sublayers of VMD.
Bagging-based ensemble OSELM is proposed to forecast high-frequency sublayers of VMD.
Two experiments were conducted on 10 min datasets from the National Renewable Energy Laboratory (NREL).
The performances of the proposed model were compared with various representative models.
The experimental results indicate that the proposed model has better accuracy than the comparison models.
Among the thirteen models, the proposed VMD-BBA-EnsOSELM model can obtain the best prediction accuracy, and the mean absolute percent error (MAPE) is always less than 0.09.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Quan, Jicheng& Shang, Li. 2020. Short-Term Wind Speed Forecasting Based on Ensemble Online Sequential Extreme Learning Machine and Bayesian Optimization. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1197784
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Quan, Jicheng& Shang, Li. Short-Term Wind Speed Forecasting Based on Ensemble Online Sequential Extreme Learning Machine and Bayesian Optimization. Mathematical Problems in Engineering No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1197784
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Quan, Jicheng& Shang, Li. Short-Term Wind Speed Forecasting Based on Ensemble Online Sequential Extreme Learning Machine and Bayesian Optimization. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1197784
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1197784
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
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